| Name | mcp-cli JSON |
| Version |
0.8.6
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| home_page | None |
| Summary | A cli for the Model Context Provider |
| upload_time | 2025-10-19 23:55:16 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | MIT |
| keywords |
llm
openai
claude
mcp
cli
|
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# MCP CLI - Model Context Protocol Command Line Interface
A powerful, feature-rich command-line interface for interacting with Model Context Protocol servers. This client enables seamless communication with LLMs through integration with the [CHUK Tool Processor](https://github.com/chrishayuk/chuk-tool-processor) and [CHUK-LLM](https://github.com/chrishayuk/chuk-llm), providing tool usage, conversation management, and multiple operational modes.
**Default Configuration**: MCP CLI defaults to using Ollama with the `gpt-oss` reasoning model for local, privacy-focused operation without requiring API keys.
## 🔄 Architecture Overview
The MCP CLI is built on a modular architecture with clean separation of concerns:
- **[CHUK Tool Processor](https://github.com/chrishayuk/chuk-tool-processor)**: Async-native tool execution and MCP server communication
- **[CHUK-LLM](https://github.com/chrishayuk/chuk-llm)**: Unified LLM provider configuration and client management with 200+ auto-generated functions
- **[CHUK-Term](https://github.com/chrishayuk/chuk-term)**: Enhanced terminal UI with themes, cross-platform terminal management, and rich formatting
- **MCP CLI**: Command orchestration and integration layer (this project)
## 🌟 Features
### Multiple Operational Modes
- **Chat Mode**: Conversational interface with streaming responses and automated tool usage (default: Ollama/gpt-oss)
- **Interactive Mode**: Command-driven shell interface for direct server operations
- **Command Mode**: Unix-friendly mode for scriptable automation and pipelines
- **Direct Commands**: Run individual commands without entering interactive mode
### Advanced Chat Interface
- **Streaming Responses**: Real-time response generation with live UI updates
- **Reasoning Visibility**: See AI's thinking process with reasoning models (gpt-oss, GPT-5, Claude 4)
- **Concurrent Tool Execution**: Execute multiple tools simultaneously while preserving conversation order
- **Smart Interruption**: Interrupt streaming responses or tool execution with Ctrl+C
- **Performance Metrics**: Response timing, words/second, and execution statistics
- **Rich Formatting**: Markdown rendering, syntax highlighting, and progress indicators
### Comprehensive Provider Support
MCP CLI supports all providers and models from CHUK-LLM, including cutting-edge reasoning models:
| Provider | Key Models | Special Features |
|----------|------------|------------------|
| **Ollama** (Default) | 🧠 gpt-oss, llama3.3, llama3.2, qwen3, qwen2.5-coder, deepseek-coder, granite3.3, mistral, gemma3, phi3, codellama | Local reasoning models, privacy-focused, no API key required |
| **OpenAI** | 🚀 GPT-5 family (gpt-5, gpt-5-mini, gpt-5-nano), GPT-4o family, O3 series (o3, o3-mini) | Advanced reasoning, function calling, vision |
| **Anthropic** | 🧠 Claude 4 family (claude-4-1-opus, claude-4-sonnet), Claude 3.5 Sonnet | Enhanced reasoning, long context |
| **Azure OpenAI** 🏢 | Enterprise GPT-5, GPT-4 models | Private endpoints, compliance, audit logs |
| **Google Gemini** | Gemini 2.0 Flash, Gemini 1.5 Pro | Multimodal, fast inference |
| **Groq** ⚡ | Llama 3.1 models, Mixtral | Ultra-fast inference (500+ tokens/sec) |
| **Perplexity** 🌐 | Sonar models | Real-time web search with citations |
| **IBM watsonx** 🏢 | Granite, Llama models | Enterprise compliance |
| **Mistral AI** 🇪🇺 | Mistral Large, Medium | European, efficient models |
### Robust Tool System
- **Automatic Discovery**: Server-provided tools are automatically detected and catalogued
- **Provider Adaptation**: Tool names are automatically sanitized for provider compatibility
- **Concurrent Execution**: Multiple tools can run simultaneously with proper coordination
- **Rich Progress Display**: Real-time progress indicators and execution timing
- **Tool History**: Complete audit trail of all tool executions
- **Streaming Tool Calls**: Support for tools that return streaming data
### Advanced Configuration Management
- **Environment Integration**: API keys and settings via environment variables
- **File-based Config**: YAML and JSON configuration files
- **User Preferences**: Persistent settings for active providers and models
- **Validation & Diagnostics**: Built-in provider health checks and configuration validation
### Enhanced User Experience
- **Cross-Platform Support**: Windows, macOS, and Linux with platform-specific optimizations via chuk-term
- **Rich Console Output**: Powered by chuk-term with 8 built-in themes (default, dark, light, minimal, terminal, monokai, dracula, solarized)
- **Advanced Terminal Management**: Cross-platform terminal operations including clearing, resizing, color detection, and cursor control
- **Interactive UI Components**: User input handling through chuk-term's prompt system (ask, confirm, select_from_list, select_multiple)
- **Command Completion**: Context-aware tab completion for all interfaces
- **Comprehensive Help**: Detailed help system with examples and usage patterns
- **Graceful Error Handling**: User-friendly error messages with troubleshooting hints
## 📋 Prerequisites
- **Python 3.11 or higher**
- **For Local Operation (Default)**:
- Ollama: Install from [ollama.ai](https://ollama.ai)
- Pull the default reasoning model: `ollama pull gpt-oss`
- **For Cloud Providers** (Optional):
- OpenAI: `OPENAI_API_KEY` environment variable (for GPT-5, GPT-4, O3 models)
- Anthropic: `ANTHROPIC_API_KEY` environment variable (for Claude 4, Claude 3.5)
- Azure: `AZURE_OPENAI_API_KEY` and `AZURE_OPENAI_ENDPOINT` (for enterprise GPT-5)
- Google: `GEMINI_API_KEY` (for Gemini models)
- Groq: `GROQ_API_KEY` (for fast Llama models)
- Custom providers: Provider-specific configuration
- **MCP Servers**: Server configuration file (default: `server_config.json`)
## 🚀 Installation
### Quick Start with Ollama (Default)
1. **Install Ollama** (if not already installed):
```bash
# macOS/Linux
curl -fsSL https://ollama.ai/install.sh | sh
# Or visit https://ollama.ai for other installation methods
```
2. **Pull the default reasoning model**:
```bash
ollama pull gpt-oss # Open-source reasoning model with thinking visibility
```
3. **Install and run MCP CLI**:
```bash
# Using uvx (recommended)
uvx mcp-cli --help
# Or install from source
git clone https://github.com/chrishayuk/mcp-cli
cd mcp-cli
pip install -e "."
mcp-cli --help
```
### Using Different Models
```bash
# === LOCAL MODELS (No API Key Required) ===
# Use default reasoning model (gpt-oss)
mcp-cli --server sqlite
# Use other Ollama models
mcp-cli --model llama3.3 # Latest Llama
mcp-cli --model qwen2.5-coder # Coding-focused
mcp-cli --model deepseek-coder # Another coding model
mcp-cli --model granite3.3 # IBM Granite
# === CLOUD PROVIDERS (API Keys Required) ===
# GPT-5 Family (requires OpenAI API key)
mcp-cli --provider openai --model gpt-5 # Full GPT-5 with reasoning
mcp-cli --provider openai --model gpt-5-mini # Efficient GPT-5 variant
mcp-cli --provider openai --model gpt-5-nano # Ultra-lightweight GPT-5
# GPT-4 Family
mcp-cli --provider openai --model gpt-4o # GPT-4 Optimized
mcp-cli --provider openai --model gpt-4o-mini # Smaller GPT-4
# O3 Reasoning Models
mcp-cli --provider openai --model o3 # O3 reasoning
mcp-cli --provider openai --model o3-mini # Efficient O3
# Claude 4 Family (requires Anthropic API key)
mcp-cli --provider anthropic --model claude-4-1-opus # Most advanced Claude
mcp-cli --provider anthropic --model claude-4-sonnet # Balanced Claude 4
mcp-cli --provider anthropic --model claude-3-5-sonnet # Claude 3.5
# Enterprise Azure (requires Azure configuration)
mcp-cli --provider azure_openai --model gpt-5 # Enterprise GPT-5
# Other Providers
mcp-cli --provider gemini --model gemini-2.0-flash # Google Gemini
mcp-cli --provider groq --model llama-3.1-70b # Fast Llama via Groq
```
## 🧰 Global Configuration
### Default Configuration
MCP CLI defaults to:
- **Provider**: `ollama` (local, no API key required)
- **Model**: `gpt-oss` (open-source reasoning model with thinking visibility)
### Command-line Arguments
Global options available for all modes and commands:
- `--server`: Specify server(s) to connect to (comma-separated)
- `--config-file`: Path to server configuration file (default: `server_config.json`)
- `--provider`: LLM provider (default: `ollama`)
- `--model`: Specific model to use (default: `gpt-oss` for Ollama)
- `--disable-filesystem`: Disable filesystem access (default: enabled)
- `--api-base`: Override API endpoint URL
- `--api-key`: Override API key (not needed for Ollama)
- `--verbose`: Enable detailed logging
- `--quiet`: Suppress non-essential output
### Environment Variables
```bash
# Override defaults
export LLM_PROVIDER=ollama # Default provider (already the default)
export LLM_MODEL=gpt-oss # Default model (already the default)
# For cloud providers (optional)
export OPENAI_API_KEY=sk-... # For GPT-5, GPT-4, O3 models
export ANTHROPIC_API_KEY=sk-ant-... # For Claude 4, Claude 3.5
export AZURE_OPENAI_API_KEY=sk-... # For enterprise GPT-5
export AZURE_OPENAI_ENDPOINT=https://...
export GEMINI_API_KEY=... # For Gemini models
export GROQ_API_KEY=... # For Groq fast inference
# Tool configuration
export MCP_TOOL_TIMEOUT=120 # Tool execution timeout (seconds)
```
## 🌐 Available Modes
### 1. Chat Mode (Default)
Provides a natural language interface with streaming responses and automatic tool usage:
```bash
# Default mode with Ollama/gpt-oss reasoning model (no API key needed)
mcp-cli --server sqlite
# See the AI's thinking process with reasoning models
mcp-cli --server sqlite --model gpt-oss # Open-source reasoning
mcp-cli --server sqlite --provider openai --model gpt-5 # GPT-5 reasoning
mcp-cli --server sqlite --provider anthropic --model claude-4-1-opus # Claude 4 reasoning
# Use different local models
mcp-cli --server sqlite --model llama3.3
mcp-cli --server sqlite --model qwen2.5-coder
# Switch to cloud providers (requires API keys)
mcp-cli chat --server sqlite --provider openai --model gpt-5
mcp-cli chat --server sqlite --provider anthropic --model claude-4-sonnet
```
### 2. Interactive Mode
Command-driven shell interface for direct server operations:
```bash
mcp-cli interactive --server sqlite
# With specific models
mcp-cli interactive --server sqlite --model gpt-oss # Local reasoning
mcp-cli interactive --server sqlite --provider openai --model gpt-5 # Cloud GPT-5
```
### 3. Command Mode
Unix-friendly interface for automation and scripting:
```bash
# Process text with reasoning models
mcp-cli cmd --server sqlite --model gpt-oss --prompt "Think through this step by step" --input data.txt
# Use GPT-5 for complex reasoning
mcp-cli cmd --server sqlite --provider openai --model gpt-5 --prompt "Analyze this data" --input data.txt
# Execute tools directly
mcp-cli cmd --server sqlite --tool list_tables --output tables.json
# Pipeline-friendly processing
echo "SELECT * FROM users LIMIT 5" | mcp-cli cmd --server sqlite --tool read_query --input -
```
### 4. Direct Commands
Execute individual commands without entering interactive mode:
```bash
# List available tools
mcp-cli tools --server sqlite
# Show provider configuration
mcp-cli provider list
# Show available models for current provider
mcp-cli models
# Show models for specific provider
mcp-cli models openai # Shows GPT-5, GPT-4, O3 models
mcp-cli models anthropic # Shows Claude 4, Claude 3.5 models
mcp-cli models ollama # Shows gpt-oss, llama3.3, etc.
# Ping servers
mcp-cli ping --server sqlite
# List resources
mcp-cli resources --server sqlite
# UI Theme Management
mcp-cli theme # Show current theme and list available
mcp-cli theme dark # Switch to dark theme
mcp-cli theme --select # Interactive theme selector
mcp-cli theme --list # List all available themes
```
## 🤖 Using Chat Mode
Chat mode provides the most advanced interface with streaming responses and intelligent tool usage.
### Starting Chat Mode
```bash
# Simple startup with default reasoning model (gpt-oss)
mcp-cli --server sqlite
# Multiple servers
mcp-cli --server sqlite,filesystem
# With advanced reasoning models
mcp-cli --server sqlite --provider openai --model gpt-5
mcp-cli --server sqlite --provider anthropic --model claude-4-1-opus
```
### Chat Commands (Slash Commands)
#### Provider & Model Management
```bash
/provider # Show current configuration (default: ollama)
/provider list # List all providers
/provider config # Show detailed configuration
/provider diagnostic # Test provider connectivity
/provider set ollama api_base http://localhost:11434 # Configure Ollama endpoint
/provider openai # Switch to OpenAI (requires API key)
/provider anthropic # Switch to Anthropic (requires API key)
/provider openai gpt-5 # Switch to OpenAI GPT-5
# Custom Provider Management
/provider custom # List custom providers
/provider add localai http://localhost:8080/v1 gpt-4 # Add custom provider
/provider remove localai # Remove custom provider
/model # Show current model (default: gpt-oss)
/model llama3.3 # Switch to different Ollama model
/model gpt-5 # Switch to GPT-5 (if using OpenAI)
/model claude-4-1-opus # Switch to Claude 4 (if using Anthropic)
/models # List available models for current provider
```
#### Tool Management
```bash
/tools # List available tools
/tools --all # Show detailed tool information
/tools --raw # Show raw JSON definitions
/tools call # Interactive tool execution
/toolhistory # Show tool execution history
/th -n 5 # Last 5 tool calls
/th 3 # Details for call #3
/th --json # Full history as JSON
```
#### Server Management (Runtime Configuration)
```bash
/server # List all configured servers
/server list # List servers (alias)
/server list all # Include disabled servers
# Add servers at runtime (persists in ~/.mcp-cli/preferences.json)
/server add <name> stdio <command> [args...]
/server add sqlite stdio uvx mcp-server-sqlite --db-path test.db
/server add playwright stdio npx @playwright/mcp@latest
/server add time stdio uvx mcp-server-time
/server add fs stdio npx @modelcontextprotocol/server-filesystem /path/to/dir
# HTTP/SSE server examples with authentication
/server add github --transport http --header "Authorization: Bearer ghp_token" -- https://api.github.com/mcp
/server add myapi --transport http --env API_KEY=secret -- https://api.example.com/mcp
/server add events --transport sse -- https://events.example.com/sse
# Manage server state
/server enable <name> # Enable a disabled server
/server disable <name> # Disable without removing
/server remove <name> # Remove user-added server
/server ping <name> # Test server connectivity
# Server details
/server <name> # Show server configuration details
```
**Note**: Servers added via `/server add` are stored in `~/.mcp-cli/preferences.json` and persist across sessions. Project servers remain in `server_config.json`.
#### Conversation Management
```bash
/conversation # Show conversation history
/ch -n 10 # Last 10 messages
/ch 5 # Details for message #5
/ch --json # Full history as JSON
/save conversation.json # Save conversation to file
/compact # Summarize conversation
/clear # Clear conversation history
/cls # Clear screen only
```
#### UI Customization
```bash
/theme # Interactive theme selector with preview
/theme dark # Switch to dark theme
/theme monokai # Switch to monokai theme
# Available themes: default, dark, light, minimal, terminal, monokai, dracula, solarized
# Themes are persisted across sessions
```
#### Session Control
```bash
/verbose # Toggle verbose/compact display (Default: Enabled)
/confirm # Toggle tool call confirmation (Default: Enabled)
/interrupt # Stop running operations
/server # Manage MCP servers (see Server Management above)
/help # Show all commands
/help tools # Help for specific command
/exit # Exit chat mode
```
### Chat Features
#### Streaming Responses with Reasoning Visibility
- **🧠 Reasoning Models**: See the AI's thinking process with gpt-oss, GPT-5, Claude 4
- **Real-time Generation**: Watch text appear token by token
- **Performance Metrics**: Words/second, response time
- **Graceful Interruption**: Ctrl+C to stop streaming
- **Progressive Rendering**: Markdown formatted as it streams
#### Tool Execution
- Automatic tool discovery and usage
- Concurrent execution with progress indicators
- Verbose and compact display modes
- Complete execution history and timing
#### Provider Integration
- Seamless switching between providers
- Model-specific optimizations
- API key and endpoint management
- Health monitoring and diagnostics
## 🖥️ Using Interactive Mode
Interactive mode provides a command shell for direct server interaction.
### Starting Interactive Mode
```bash
mcp-cli interactive --server sqlite
```
### Interactive Commands
```bash
help # Show available commands
exit # Exit interactive mode
clear # Clear terminal
# Provider management
provider # Show current provider
provider list # List providers
provider anthropic # Switch provider
provider openai gpt-5 # Switch to GPT-5
# Model management
model # Show current model
model gpt-oss # Switch to reasoning model
model claude-4-1-opus # Switch to Claude 4
models # List available models
# Tool operations
tools # List tools
tools --all # Detailed tool info
tools call # Interactive tool execution
# Server operations
servers # List servers
ping # Ping all servers
resources # List resources
prompts # List prompts
```
## 📄 Using Command Mode
Command mode provides Unix-friendly automation capabilities.
### Command Mode Options
```bash
--input FILE # Input file (- for stdin)
--output FILE # Output file (- for stdout)
--prompt TEXT # Prompt template
--tool TOOL # Execute specific tool
--tool-args JSON # Tool arguments as JSON
--system-prompt TEXT # Custom system prompt
--raw # Raw output without formatting
--single-turn # Disable multi-turn conversation
--max-turns N # Maximum conversation turns
```
### Examples
```bash
# Text processing with reasoning models
echo "Analyze this data" | mcp-cli cmd --server sqlite --model gpt-oss --input - --output analysis.txt
# Use GPT-5 for complex analysis
mcp-cli cmd --server sqlite --provider openai --model gpt-5 --prompt "Provide strategic analysis" --input report.txt
# Tool execution
mcp-cli cmd --server sqlite --tool list_tables --raw
# Complex queries
mcp-cli cmd --server sqlite --tool read_query --tool-args '{"query": "SELECT COUNT(*) FROM users"}'
# Batch processing with GNU Parallel
ls *.txt | parallel mcp-cli cmd --server sqlite --input {} --output {}.summary --prompt "Summarize: {{input}}"
```
## 🔧 Provider Configuration
### Ollama Configuration (Default)
Ollama runs locally by default on `http://localhost:11434`. To use reasoning and other models:
```bash
# Pull reasoning and other models for Ollama
ollama pull gpt-oss # Default reasoning model
ollama pull llama3.3 # Latest Llama
ollama pull llama3.2 # Llama 3.2
ollama pull qwen3 # Qwen 3
ollama pull qwen2.5-coder # Coding-focused
ollama pull deepseek-coder # DeepSeek coder
ollama pull granite3.3 # IBM Granite
ollama pull mistral # Mistral
ollama pull gemma3 # Google Gemma
ollama pull phi3 # Microsoft Phi
ollama pull codellama # Code Llama
# List available Ollama models
ollama list
# Configure remote Ollama server
mcp-cli provider set ollama api_base http://remote-server:11434
```
### Cloud Provider Configuration
To use cloud providers with advanced models, configure API keys:
```bash
# Configure OpenAI (for GPT-5, GPT-4, O3 models)
mcp-cli provider set openai api_key sk-your-key-here
# Configure Anthropic (for Claude 4, Claude 3.5)
mcp-cli provider set anthropic api_key sk-ant-your-key-here
# Configure Azure OpenAI (for enterprise GPT-5)
mcp-cli provider set azure_openai api_key sk-your-key-here
mcp-cli provider set azure_openai api_base https://your-resource.openai.azure.com
# Configure other providers
mcp-cli provider set gemini api_key your-gemini-key
mcp-cli provider set groq api_key your-groq-key
# Test configuration
mcp-cli provider diagnostic openai
mcp-cli provider diagnostic anthropic
```
### Custom OpenAI-Compatible Providers
MCP CLI supports adding custom OpenAI-compatible providers (LocalAI, custom proxies, etc.):
```bash
# Add a custom provider (persisted across sessions)
mcp-cli provider add localai http://localhost:8080/v1 gpt-4 gpt-3.5-turbo
mcp-cli provider add myproxy https://proxy.example.com/v1 custom-model-1 custom-model-2
# Set API key via environment variable (never stored in config)
export LOCALAI_API_KEY=your-api-key
export MYPROXY_API_KEY=your-api-key
# List custom providers
mcp-cli provider custom
# Use custom provider
mcp-cli --provider localai --server sqlite
mcp-cli --provider myproxy --model custom-model-1 --server sqlite
# Remove custom provider
mcp-cli provider remove localai
# Runtime provider (session-only, not persisted)
mcp-cli --provider temp-ai --api-base https://api.temp.com/v1 --api-key test-key --server sqlite
```
**Security Note**: API keys are NEVER stored in configuration files. Use environment variables following the pattern `{PROVIDER_NAME}_API_KEY` or pass via `--api-key` for session-only use.
### Manual Configuration
The `chuk_llm` library configuration in `~/.chuk_llm/config.yaml`:
```yaml
ollama:
api_base: http://localhost:11434
default_model: gpt-oss
openai:
api_base: https://api.openai.com/v1
default_model: gpt-5
anthropic:
api_base: https://api.anthropic.com
default_model: claude-4-1-opus
azure_openai:
api_base: https://your-resource.openai.azure.com
default_model: gpt-5
gemini:
api_base: https://generativelanguage.googleapis.com
default_model: gemini-2.0-flash
groq:
api_base: https://api.groq.com
default_model: llama-3.1-70b
```
API keys (if using cloud providers) in `~/.chuk_llm/.env`:
```bash
OPENAI_API_KEY=sk-your-key-here
ANTHROPIC_API_KEY=sk-ant-your-key-here
AZURE_OPENAI_API_KEY=sk-your-azure-key-here
GEMINI_API_KEY=your-gemini-key
GROQ_API_KEY=your-groq-key
```
## 📂 Server Configuration
MCP CLI supports two types of server configurations:
1. **Project Servers** (`server_config.json`): Shared project-level configurations
2. **User Servers** (`~/.mcp-cli/preferences.json`): Personal runtime-added servers that persist across sessions
### Project Configuration
Create a `server_config.json` file with your MCP server configurations:
```json
{
"mcpServers": {
"sqlite": {
"command": "python",
"args": ["-m", "mcp_server.sqlite_server"],
"env": {
"DATABASE_PATH": "database.db"
}
},
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/files"],
"env": {}
},
"brave-search": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-brave-search"],
"env": {
"BRAVE_API_KEY": "your-brave-api-key"
}
}
}
}
```
### Runtime Server Management
Add servers dynamically during runtime without editing configuration files:
```bash
# Add STDIO servers (most common)
mcp-cli
> /server add sqlite stdio uvx mcp-server-sqlite --db-path mydata.db
> /server add playwright stdio npx @playwright/mcp@latest
> /server add time stdio uvx mcp-server-time
# Add HTTP servers with authentication
> /server add github --transport http --header "Authorization: Bearer ghp_token" -- https://api.github.com/mcp
> /server add myapi --transport http --env API_KEY=secret -- https://api.example.com/mcp
# Add SSE (Server-Sent Events) servers
> /server add events --transport sse -- https://events.example.com/sse
# Manage servers
> /server list # Show all servers
> /server disable sqlite # Temporarily disable
> /server enable sqlite # Re-enable
> /server remove myapi # Remove user-added server
```
**Key Points:**
- User-added servers persist in `~/.mcp-cli/preferences.json`
- Survive application restarts
- Can be enabled/disabled without removal
- Support STDIO, HTTP, and SSE transports
- Environment variables and headers for authentication
## 📈 Advanced Usage Examples
### Reasoning Model Comparison
```bash
# Compare reasoning across different models
> /provider ollama
> /model gpt-oss
> Think through this problem step by step: If a train leaves New York at 3 PM...
[See the complete thinking process with gpt-oss]
> /provider openai
> /model gpt-5
> Think through this problem step by step: If a train leaves New York at 3 PM...
[See GPT-5's reasoning approach]
> /provider anthropic
> /model claude-4-1-opus
> Think through this problem step by step: If a train leaves New York at 3 PM...
[See Claude 4's analytical process]
```
### Local-First Workflow with Reasoning
```bash
# Start with default Ollama/gpt-oss (no API key needed)
mcp-cli chat --server sqlite
# Use reasoning model for complex problems
> Think through this database optimization problem step by step
[gpt-oss shows its complete thinking process before answering]
# Try different local models for different tasks
> /model llama3.3 # General purpose
> /model qwen2.5-coder # For coding tasks
> /model deepseek-coder # Alternative coding model
> /model granite3.3 # IBM's model
> /model gpt-oss # Back to reasoning model
# Switch to cloud when needed (requires API keys)
> /provider openai
> /model gpt-5
> Complex enterprise architecture design...
> /provider anthropic
> /model claude-4-1-opus
> Detailed strategic analysis...
> /provider ollama
> /model gpt-oss
> Continue with local processing...
```
### Multi-Provider Workflow
```bash
# Start with local reasoning (default, no API key)
mcp-cli chat --server sqlite
# Compare responses across providers
> /provider ollama
> What's the best way to optimize this SQL query?
> /provider openai gpt-5 # Requires API key
> What's the best way to optimize this SQL query?
> /provider anthropic claude-4-sonnet # Requires API key
> What's the best way to optimize this SQL query?
# Use each provider's strengths
> /provider ollama gpt-oss # Local reasoning, privacy
> /provider openai gpt-5 # Advanced reasoning
> /provider anthropic claude-4-1-opus # Deep analysis
> /provider groq llama-3.1-70b # Ultra-fast responses
```
### Complex Tool Workflows with Reasoning
```bash
# Use reasoning model for complex database tasks
> /model gpt-oss
> I need to analyze our database performance. Think through what we should check first.
[gpt-oss shows thinking: "First, I should check the table structure, then indexes, then query patterns..."]
[Tool: list_tables] → products, customers, orders
> Now analyze the indexes and suggest optimizations
[gpt-oss thinks through index analysis]
[Tool: describe_table] → Shows current indexes
[Tool: read_query] → Analyzes query patterns
> Create an optimization plan based on your analysis
[Complete reasoning process followed by specific recommendations]
```
### Automation and Scripting
```bash
# Batch processing with different models
for file in data/*.csv; do
# Use reasoning model for analysis
mcp-cli cmd --server sqlite \
--model gpt-oss \
--prompt "Analyze this data and think through patterns" \
--input "$file" \
--output "analysis/$(basename "$file" .csv)_reasoning.txt"
# Use coding model for generating scripts
mcp-cli cmd --server sqlite \
--model qwen2.5-coder \
--prompt "Generate Python code to process this data" \
--input "$file" \
--output "scripts/$(basename "$file" .csv)_script.py"
done
# Pipeline with reasoning
cat complex_problem.txt | \
mcp-cli cmd --model gpt-oss --prompt "Think through this step by step" --input - | \
mcp-cli cmd --model llama3.3 --prompt "Summarize the key points" --input - > solution.txt
```
### Performance Monitoring
```bash
# Check provider and model performance
> /provider diagnostic
Provider Diagnostics
Provider | Status | Response Time | Features | Models
ollama | ✅ Ready | 56ms | 📡🔧 | gpt-oss, llama3.3, qwen3, ...
openai | ✅ Ready | 234ms | 📡🔧👁️ | gpt-5, gpt-4o, o3, ...
anthropic | ✅ Ready | 187ms | 📡🔧 | claude-4-1-opus, claude-4-sonnet, ...
azure_openai | ✅ Ready | 198ms | 📡🔧👁️ | gpt-5, gpt-4o, ...
gemini | ✅ Ready | 156ms | 📡🔧👁️ | gemini-2.0-flash, ...
groq | ✅ Ready | 45ms | 📡🔧 | llama-3.1-70b, ...
# Check available models
> /models
Models for ollama (Current Provider)
Model | Status
gpt-oss | Current & Default (Reasoning)
llama3.3 | Available
llama3.2 | Available
qwen2.5-coder | Available
deepseek-coder | Available
granite3.3 | Available
... and 6 more
# Monitor tool execution with reasoning
> /verbose
> /model gpt-oss
> Analyze the database and optimize the slowest queries
[Shows complete thinking process]
[Tool execution with timing]
```
## 🔍 Troubleshooting
### Common Issues
1. **Ollama not running** (default provider):
```bash
# Start Ollama service
ollama serve
# Or check if it's running
curl http://localhost:11434/api/tags
```
2. **Model not found**:
```bash
# For Ollama (default), pull the model first
ollama pull gpt-oss # Reasoning model
ollama pull llama3.3 # Latest Llama
ollama pull qwen2.5-coder # Coding model
# List available models
ollama list
# For cloud providers, check supported models
mcp-cli models openai # Shows GPT-5, GPT-4, O3 models
mcp-cli models anthropic # Shows Claude 4, Claude 3.5 models
```
3. **Provider not found or API key missing**:
```bash
# Check available providers
mcp-cli provider list
# For cloud providers, set API keys
mcp-cli provider set openai api_key sk-your-key
mcp-cli provider set anthropic api_key sk-ant-your-key
# Test connection
mcp-cli provider diagnostic openai
```
4. **Connection issues with Ollama**:
```bash
# Check Ollama is running
ollama list
# Test connection
mcp-cli provider diagnostic ollama
# Configure custom endpoint if needed
mcp-cli provider set ollama api_base http://localhost:11434
```
### Debug Mode
Enable verbose logging for troubleshooting:
```bash
mcp-cli --verbose chat --server sqlite
mcp-cli --log-level DEBUG interactive --server sqlite
```
## 🔒 Security Considerations
- **Local by Default**: Ollama with gpt-oss runs locally, keeping your data private
- **API Keys**: Only needed for cloud providers (OpenAI, Anthropic, etc.), stored securely
- **File Access**: Filesystem access can be disabled with `--disable-filesystem`
- **Tool Validation**: All tool calls are validated before execution
- **Timeout Protection**: Configurable timeouts prevent hanging operations
- **Server Isolation**: Each server runs in its own process
## 🚀 Performance Features
- **Local Processing**: Default Ollama provider minimizes latency
- **Reasoning Visibility**: See AI thinking process with gpt-oss, GPT-5, Claude 4
- **Concurrent Tool Execution**: Multiple tools can run simultaneously
- **Streaming Responses**: Real-time response generation
- **Connection Pooling**: Efficient reuse of client connections
- **Caching**: Tool metadata and provider configurations are cached
- **Async Architecture**: Non-blocking operations throughout
## 📦 Dependencies
Core dependencies are organized into feature groups:
- **cli**: Terminal UI and command framework (Rich, Typer, chuk-term)
- **dev**: Development tools, testing utilities, linting
- **chuk-tool-processor**: Core tool execution and MCP communication
- **chuk-llm**: Unified LLM provider management with 200+ auto-generated functions
- **chuk-term**: Enhanced terminal UI with themes, prompts, and cross-platform support
Install with specific features:
```bash
pip install "mcp-cli[cli]" # Basic CLI features
pip install "mcp-cli[cli,dev]" # CLI with development tools
```
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Setup
```bash
git clone https://github.com/chrishayuk/mcp-cli
cd mcp-cli
pip install -e ".[cli,dev]"
pre-commit install
```
### Demo Scripts
Explore the capabilities of MCP CLI:
```bash
# Custom Provider Management Demos
# Interactive walkthrough demo (educational)
uv run examples/custom_provider_demo.py
# Working demo with actual inference (requires OPENAI_API_KEY)
uv run examples/custom_provider_working_demo.py
# Simple shell script demo (requires OPENAI_API_KEY)
bash examples/custom_provider_simple_demo.sh
# Terminal management features (chuk-term)
uv run examples/ui_terminal_demo.py
# Output system with themes (chuk-term)
uv run examples/ui_output_demo.py
# Streaming UI capabilities (chuk-term)
uv run examples/ui_streaming_demo.py
```
### Running Tests
```bash
pytest
pytest --cov=mcp_cli --cov-report=html
```
## 📜 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- **[CHUK Tool Processor](https://github.com/chrishayuk/chuk-tool-processor)** - Async-native tool execution
- **[CHUK-LLM](https://github.com/chrishayuk/chuk-llm)** - Unified LLM provider management with GPT-5, Claude 4, and reasoning model support
- **[CHUK-Term](https://github.com/chrishayuk/chuk-term)** - Enhanced terminal UI with themes and cross-platform support
- **[Rich](https://github.com/Textualize/rich)** - Beautiful terminal formatting
- **[Typer](https://typer.tiangolo.com/)** - CLI framework
- **[Prompt Toolkit](https://github.com/prompt-toolkit/python-prompt-toolkit)** - Interactive input
## 🔗 Related Projects
- **[Model Context Protocol](https://modelcontextprotocol.io/)** - Core protocol specification
- **[MCP Servers](https://github.com/modelcontextprotocol/servers)** - Official MCP server implementations
- **[CHUK Tool Processor](https://github.com/chrishayuk/chuk-tool-processor)** - Tool execution engine
- **[CHUK-LLM](https://github.com/chrishayuk/chuk-llm)** - LLM provider abstraction with GPT-5, Claude 4, O3 series support
- **[CHUK-Term](https://github.com/chrishayuk/chuk-term)** - Terminal UI library with themes and cross-platform support
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"description": "# MCP CLI - Model Context Protocol Command Line Interface\n\nA powerful, feature-rich command-line interface for interacting with Model Context Protocol servers. This client enables seamless communication with LLMs through integration with the [CHUK Tool Processor](https://github.com/chrishayuk/chuk-tool-processor) and [CHUK-LLM](https://github.com/chrishayuk/chuk-llm), providing tool usage, conversation management, and multiple operational modes.\n\n**Default Configuration**: MCP CLI defaults to using Ollama with the `gpt-oss` reasoning model for local, privacy-focused operation without requiring API keys.\n\n## \ud83d\udd04 Architecture Overview\n\nThe MCP CLI is built on a modular architecture with clean separation of concerns:\n\n- **[CHUK Tool Processor](https://github.com/chrishayuk/chuk-tool-processor)**: Async-native tool execution and MCP server communication\n- **[CHUK-LLM](https://github.com/chrishayuk/chuk-llm)**: Unified LLM provider configuration and client management with 200+ auto-generated functions\n- **[CHUK-Term](https://github.com/chrishayuk/chuk-term)**: Enhanced terminal UI with themes, cross-platform terminal management, and rich formatting\n- **MCP CLI**: Command orchestration and integration layer (this project)\n\n## \ud83c\udf1f Features\n\n### Multiple Operational Modes\n- **Chat Mode**: Conversational interface with streaming responses and automated tool usage (default: Ollama/gpt-oss)\n- **Interactive Mode**: Command-driven shell interface for direct server operations\n- **Command Mode**: Unix-friendly mode for scriptable automation and pipelines\n- **Direct Commands**: Run individual commands without entering interactive mode\n\n### Advanced Chat Interface\n- **Streaming Responses**: Real-time response generation with live UI updates\n- **Reasoning Visibility**: See AI's thinking process with reasoning models (gpt-oss, GPT-5, Claude 4)\n- **Concurrent Tool Execution**: Execute multiple tools simultaneously while preserving conversation order\n- **Smart Interruption**: Interrupt streaming responses or tool execution with Ctrl+C\n- **Performance Metrics**: Response timing, words/second, and execution statistics\n- **Rich Formatting**: Markdown rendering, syntax highlighting, and progress indicators\n\n### Comprehensive Provider Support\n\nMCP CLI supports all providers and models from CHUK-LLM, including cutting-edge reasoning models:\n\n| Provider | Key Models | Special Features |\n|----------|------------|------------------|\n| **Ollama** (Default) | \ud83e\udde0 gpt-oss, llama3.3, llama3.2, qwen3, qwen2.5-coder, deepseek-coder, granite3.3, mistral, gemma3, phi3, codellama | Local reasoning models, privacy-focused, no API key required |\n| **OpenAI** | \ud83d\ude80 GPT-5 family (gpt-5, gpt-5-mini, gpt-5-nano), GPT-4o family, O3 series (o3, o3-mini) | Advanced reasoning, function calling, vision |\n| **Anthropic** | \ud83e\udde0 Claude 4 family (claude-4-1-opus, claude-4-sonnet), Claude 3.5 Sonnet | Enhanced reasoning, long context |\n| **Azure OpenAI** \ud83c\udfe2 | Enterprise GPT-5, GPT-4 models | Private endpoints, compliance, audit logs |\n| **Google Gemini** | Gemini 2.0 Flash, Gemini 1.5 Pro | Multimodal, fast inference |\n| **Groq** \u26a1 | Llama 3.1 models, Mixtral | Ultra-fast inference (500+ tokens/sec) |\n| **Perplexity** \ud83c\udf10 | Sonar models | Real-time web search with citations |\n| **IBM watsonx** \ud83c\udfe2 | Granite, Llama models | Enterprise compliance |\n| **Mistral AI** \ud83c\uddea\ud83c\uddfa | Mistral Large, Medium | European, efficient models |\n\n### Robust Tool System\n- **Automatic Discovery**: Server-provided tools are automatically detected and catalogued\n- **Provider Adaptation**: Tool names are automatically sanitized for provider compatibility\n- **Concurrent Execution**: Multiple tools can run simultaneously with proper coordination\n- **Rich Progress Display**: Real-time progress indicators and execution timing\n- **Tool History**: Complete audit trail of all tool executions\n- **Streaming Tool Calls**: Support for tools that return streaming data\n\n### Advanced Configuration Management\n- **Environment Integration**: API keys and settings via environment variables\n- **File-based Config**: YAML and JSON configuration files\n- **User Preferences**: Persistent settings for active providers and models\n- **Validation & Diagnostics**: Built-in provider health checks and configuration validation\n\n### Enhanced User Experience\n- **Cross-Platform Support**: Windows, macOS, and Linux with platform-specific optimizations via chuk-term\n- **Rich Console Output**: Powered by chuk-term with 8 built-in themes (default, dark, light, minimal, terminal, monokai, dracula, solarized)\n- **Advanced Terminal Management**: Cross-platform terminal operations including clearing, resizing, color detection, and cursor control\n- **Interactive UI Components**: User input handling through chuk-term's prompt system (ask, confirm, select_from_list, select_multiple)\n- **Command Completion**: Context-aware tab completion for all interfaces\n- **Comprehensive Help**: Detailed help system with examples and usage patterns\n- **Graceful Error Handling**: User-friendly error messages with troubleshooting hints\n\n## \ud83d\udccb Prerequisites\n\n- **Python 3.11 or higher**\n- **For Local Operation (Default)**:\n - Ollama: Install from [ollama.ai](https://ollama.ai)\n - Pull the default reasoning model: `ollama pull gpt-oss`\n- **For Cloud Providers** (Optional):\n - OpenAI: `OPENAI_API_KEY` environment variable (for GPT-5, GPT-4, O3 models)\n - Anthropic: `ANTHROPIC_API_KEY` environment variable (for Claude 4, Claude 3.5)\n - Azure: `AZURE_OPENAI_API_KEY` and `AZURE_OPENAI_ENDPOINT` (for enterprise GPT-5)\n - Google: `GEMINI_API_KEY` (for Gemini models)\n - Groq: `GROQ_API_KEY` (for fast Llama models)\n - Custom providers: Provider-specific configuration\n- **MCP Servers**: Server configuration file (default: `server_config.json`)\n\n## \ud83d\ude80 Installation\n\n### Quick Start with Ollama (Default)\n\n1. **Install Ollama** (if not already installed):\n```bash\n# macOS/Linux\ncurl -fsSL https://ollama.ai/install.sh | sh\n\n# Or visit https://ollama.ai for other installation methods\n```\n\n2. **Pull the default reasoning model**:\n```bash\nollama pull gpt-oss # Open-source reasoning model with thinking visibility\n```\n\n3. **Install and run MCP CLI**:\n```bash\n# Using uvx (recommended)\nuvx mcp-cli --help\n\n# Or install from source\ngit clone https://github.com/chrishayuk/mcp-cli\ncd mcp-cli\npip install -e \".\"\nmcp-cli --help\n```\n\n### Using Different Models\n\n```bash\n# === LOCAL MODELS (No API Key Required) ===\n\n# Use default reasoning model (gpt-oss)\nmcp-cli --server sqlite\n\n# Use other Ollama models\nmcp-cli --model llama3.3 # Latest Llama\nmcp-cli --model qwen2.5-coder # Coding-focused\nmcp-cli --model deepseek-coder # Another coding model\nmcp-cli --model granite3.3 # IBM Granite\n\n# === CLOUD PROVIDERS (API Keys Required) ===\n\n# GPT-5 Family (requires OpenAI API key)\nmcp-cli --provider openai --model gpt-5 # Full GPT-5 with reasoning\nmcp-cli --provider openai --model gpt-5-mini # Efficient GPT-5 variant\nmcp-cli --provider openai --model gpt-5-nano # Ultra-lightweight GPT-5\n\n# GPT-4 Family\nmcp-cli --provider openai --model gpt-4o # GPT-4 Optimized\nmcp-cli --provider openai --model gpt-4o-mini # Smaller GPT-4\n\n# O3 Reasoning Models\nmcp-cli --provider openai --model o3 # O3 reasoning\nmcp-cli --provider openai --model o3-mini # Efficient O3\n\n# Claude 4 Family (requires Anthropic API key)\nmcp-cli --provider anthropic --model claude-4-1-opus # Most advanced Claude\nmcp-cli --provider anthropic --model claude-4-sonnet # Balanced Claude 4\nmcp-cli --provider anthropic --model claude-3-5-sonnet # Claude 3.5\n\n# Enterprise Azure (requires Azure configuration)\nmcp-cli --provider azure_openai --model gpt-5 # Enterprise GPT-5\n\n# Other Providers\nmcp-cli --provider gemini --model gemini-2.0-flash # Google Gemini\nmcp-cli --provider groq --model llama-3.1-70b # Fast Llama via Groq\n```\n\n## \ud83e\uddf0 Global Configuration\n\n### Default Configuration\n\nMCP CLI defaults to:\n- **Provider**: `ollama` (local, no API key required)\n- **Model**: `gpt-oss` (open-source reasoning model with thinking visibility)\n\n### Command-line Arguments\n\nGlobal options available for all modes and commands:\n\n- `--server`: Specify server(s) to connect to (comma-separated)\n- `--config-file`: Path to server configuration file (default: `server_config.json`)\n- `--provider`: LLM provider (default: `ollama`)\n- `--model`: Specific model to use (default: `gpt-oss` for Ollama)\n- `--disable-filesystem`: Disable filesystem access (default: enabled)\n- `--api-base`: Override API endpoint URL\n- `--api-key`: Override API key (not needed for Ollama)\n- `--verbose`: Enable detailed logging\n- `--quiet`: Suppress non-essential output\n\n### Environment Variables\n\n```bash\n# Override defaults\nexport LLM_PROVIDER=ollama # Default provider (already the default)\nexport LLM_MODEL=gpt-oss # Default model (already the default)\n\n# For cloud providers (optional)\nexport OPENAI_API_KEY=sk-... # For GPT-5, GPT-4, O3 models\nexport ANTHROPIC_API_KEY=sk-ant-... # For Claude 4, Claude 3.5\nexport AZURE_OPENAI_API_KEY=sk-... # For enterprise GPT-5\nexport AZURE_OPENAI_ENDPOINT=https://...\nexport GEMINI_API_KEY=... # For Gemini models\nexport GROQ_API_KEY=... # For Groq fast inference\n\n# Tool configuration\nexport MCP_TOOL_TIMEOUT=120 # Tool execution timeout (seconds)\n```\n\n## \ud83c\udf10 Available Modes\n\n### 1. Chat Mode (Default)\n\nProvides a natural language interface with streaming responses and automatic tool usage:\n\n```bash\n# Default mode with Ollama/gpt-oss reasoning model (no API key needed)\nmcp-cli --server sqlite\n\n# See the AI's thinking process with reasoning models\nmcp-cli --server sqlite --model gpt-oss # Open-source reasoning\nmcp-cli --server sqlite --provider openai --model gpt-5 # GPT-5 reasoning\nmcp-cli --server sqlite --provider anthropic --model claude-4-1-opus # Claude 4 reasoning\n\n# Use different local models\nmcp-cli --server sqlite --model llama3.3\nmcp-cli --server sqlite --model qwen2.5-coder\n\n# Switch to cloud providers (requires API keys)\nmcp-cli chat --server sqlite --provider openai --model gpt-5\nmcp-cli chat --server sqlite --provider anthropic --model claude-4-sonnet\n```\n\n### 2. Interactive Mode\n\nCommand-driven shell interface for direct server operations:\n\n```bash\nmcp-cli interactive --server sqlite\n\n# With specific models\nmcp-cli interactive --server sqlite --model gpt-oss # Local reasoning\nmcp-cli interactive --server sqlite --provider openai --model gpt-5 # Cloud GPT-5\n```\n\n### 3. Command Mode\n\nUnix-friendly interface for automation and scripting:\n\n```bash\n# Process text with reasoning models\nmcp-cli cmd --server sqlite --model gpt-oss --prompt \"Think through this step by step\" --input data.txt\n\n# Use GPT-5 for complex reasoning\nmcp-cli cmd --server sqlite --provider openai --model gpt-5 --prompt \"Analyze this data\" --input data.txt\n\n# Execute tools directly\nmcp-cli cmd --server sqlite --tool list_tables --output tables.json\n\n# Pipeline-friendly processing\necho \"SELECT * FROM users LIMIT 5\" | mcp-cli cmd --server sqlite --tool read_query --input -\n```\n\n### 4. Direct Commands\n\nExecute individual commands without entering interactive mode:\n\n```bash\n# List available tools\nmcp-cli tools --server sqlite\n\n# Show provider configuration\nmcp-cli provider list\n\n# Show available models for current provider\nmcp-cli models\n\n# Show models for specific provider\nmcp-cli models openai # Shows GPT-5, GPT-4, O3 models\nmcp-cli models anthropic # Shows Claude 4, Claude 3.5 models\nmcp-cli models ollama # Shows gpt-oss, llama3.3, etc.\n\n# Ping servers\nmcp-cli ping --server sqlite\n\n# List resources\nmcp-cli resources --server sqlite\n\n# UI Theme Management\nmcp-cli theme # Show current theme and list available\nmcp-cli theme dark # Switch to dark theme\nmcp-cli theme --select # Interactive theme selector\nmcp-cli theme --list # List all available themes\n```\n\n## \ud83e\udd16 Using Chat Mode\n\nChat mode provides the most advanced interface with streaming responses and intelligent tool usage.\n\n### Starting Chat Mode\n\n```bash\n# Simple startup with default reasoning model (gpt-oss)\nmcp-cli --server sqlite\n\n# Multiple servers\nmcp-cli --server sqlite,filesystem\n\n# With advanced reasoning models\nmcp-cli --server sqlite --provider openai --model gpt-5\nmcp-cli --server sqlite --provider anthropic --model claude-4-1-opus\n```\n\n### Chat Commands (Slash Commands)\n\n#### Provider & Model Management\n```bash\n/provider # Show current configuration (default: ollama)\n/provider list # List all providers\n/provider config # Show detailed configuration\n/provider diagnostic # Test provider connectivity\n/provider set ollama api_base http://localhost:11434 # Configure Ollama endpoint\n/provider openai # Switch to OpenAI (requires API key)\n/provider anthropic # Switch to Anthropic (requires API key)\n/provider openai gpt-5 # Switch to OpenAI GPT-5\n\n# Custom Provider Management\n/provider custom # List custom providers\n/provider add localai http://localhost:8080/v1 gpt-4 # Add custom provider\n/provider remove localai # Remove custom provider\n\n/model # Show current model (default: gpt-oss)\n/model llama3.3 # Switch to different Ollama model\n/model gpt-5 # Switch to GPT-5 (if using OpenAI)\n/model claude-4-1-opus # Switch to Claude 4 (if using Anthropic)\n/models # List available models for current provider\n```\n\n#### Tool Management\n```bash\n/tools # List available tools\n/tools --all # Show detailed tool information\n/tools --raw # Show raw JSON definitions\n/tools call # Interactive tool execution\n\n/toolhistory # Show tool execution history\n/th -n 5 # Last 5 tool calls\n/th 3 # Details for call #3\n/th --json # Full history as JSON\n```\n\n#### Server Management (Runtime Configuration)\n```bash\n/server # List all configured servers\n/server list # List servers (alias)\n/server list all # Include disabled servers\n\n# Add servers at runtime (persists in ~/.mcp-cli/preferences.json)\n/server add <name> stdio <command> [args...]\n/server add sqlite stdio uvx mcp-server-sqlite --db-path test.db\n/server add playwright stdio npx @playwright/mcp@latest\n/server add time stdio uvx mcp-server-time\n/server add fs stdio npx @modelcontextprotocol/server-filesystem /path/to/dir\n\n# HTTP/SSE server examples with authentication\n/server add github --transport http --header \"Authorization: Bearer ghp_token\" -- https://api.github.com/mcp\n/server add myapi --transport http --env API_KEY=secret -- https://api.example.com/mcp\n/server add events --transport sse -- https://events.example.com/sse\n\n# Manage server state\n/server enable <name> # Enable a disabled server\n/server disable <name> # Disable without removing\n/server remove <name> # Remove user-added server\n/server ping <name> # Test server connectivity\n\n# Server details\n/server <name> # Show server configuration details\n```\n\n**Note**: Servers added via `/server add` are stored in `~/.mcp-cli/preferences.json` and persist across sessions. Project servers remain in `server_config.json`.\n\n#### Conversation Management\n```bash\n/conversation # Show conversation history\n/ch -n 10 # Last 10 messages\n/ch 5 # Details for message #5\n/ch --json # Full history as JSON\n\n/save conversation.json # Save conversation to file\n/compact # Summarize conversation\n/clear # Clear conversation history\n/cls # Clear screen only\n```\n\n#### UI Customization\n```bash\n/theme # Interactive theme selector with preview\n/theme dark # Switch to dark theme\n/theme monokai # Switch to monokai theme\n\n# Available themes: default, dark, light, minimal, terminal, monokai, dracula, solarized\n# Themes are persisted across sessions\n```\n\n#### Session Control\n```bash\n/verbose # Toggle verbose/compact display (Default: Enabled)\n/confirm # Toggle tool call confirmation (Default: Enabled)\n/interrupt # Stop running operations\n/server # Manage MCP servers (see Server Management above)\n/help # Show all commands\n/help tools # Help for specific command\n/exit # Exit chat mode\n```\n\n### Chat Features\n\n#### Streaming Responses with Reasoning Visibility\n- **\ud83e\udde0 Reasoning Models**: See the AI's thinking process with gpt-oss, GPT-5, Claude 4\n- **Real-time Generation**: Watch text appear token by token\n- **Performance Metrics**: Words/second, response time\n- **Graceful Interruption**: Ctrl+C to stop streaming\n- **Progressive Rendering**: Markdown formatted as it streams\n\n#### Tool Execution\n- Automatic tool discovery and usage\n- Concurrent execution with progress indicators\n- Verbose and compact display modes\n- Complete execution history and timing\n\n#### Provider Integration\n- Seamless switching between providers\n- Model-specific optimizations\n- API key and endpoint management\n- Health monitoring and diagnostics\n\n## \ud83d\udda5\ufe0f Using Interactive Mode\n\nInteractive mode provides a command shell for direct server interaction.\n\n### Starting Interactive Mode\n\n```bash\nmcp-cli interactive --server sqlite\n```\n\n### Interactive Commands\n\n```bash\nhelp # Show available commands\nexit # Exit interactive mode\nclear # Clear terminal\n\n# Provider management\nprovider # Show current provider\nprovider list # List providers\nprovider anthropic # Switch provider\nprovider openai gpt-5 # Switch to GPT-5\n\n# Model management\nmodel # Show current model\nmodel gpt-oss # Switch to reasoning model\nmodel claude-4-1-opus # Switch to Claude 4\nmodels # List available models\n\n# Tool operations\ntools # List tools\ntools --all # Detailed tool info\ntools call # Interactive tool execution\n\n# Server operations\nservers # List servers\nping # Ping all servers\nresources # List resources\nprompts # List prompts\n```\n\n## \ud83d\udcc4 Using Command Mode\n\nCommand mode provides Unix-friendly automation capabilities.\n\n### Command Mode Options\n\n```bash\n--input FILE # Input file (- for stdin)\n--output FILE # Output file (- for stdout)\n--prompt TEXT # Prompt template\n--tool TOOL # Execute specific tool\n--tool-args JSON # Tool arguments as JSON\n--system-prompt TEXT # Custom system prompt\n--raw # Raw output without formatting\n--single-turn # Disable multi-turn conversation\n--max-turns N # Maximum conversation turns\n```\n\n### Examples\n\n```bash\n# Text processing with reasoning models\necho \"Analyze this data\" | mcp-cli cmd --server sqlite --model gpt-oss --input - --output analysis.txt\n\n# Use GPT-5 for complex analysis\nmcp-cli cmd --server sqlite --provider openai --model gpt-5 --prompt \"Provide strategic analysis\" --input report.txt\n\n# Tool execution\nmcp-cli cmd --server sqlite --tool list_tables --raw\n\n# Complex queries\nmcp-cli cmd --server sqlite --tool read_query --tool-args '{\"query\": \"SELECT COUNT(*) FROM users\"}'\n\n# Batch processing with GNU Parallel\nls *.txt | parallel mcp-cli cmd --server sqlite --input {} --output {}.summary --prompt \"Summarize: {{input}}\"\n```\n\n## \ud83d\udd27 Provider Configuration\n\n### Ollama Configuration (Default)\n\nOllama runs locally by default on `http://localhost:11434`. To use reasoning and other models:\n\n```bash\n# Pull reasoning and other models for Ollama\nollama pull gpt-oss # Default reasoning model\nollama pull llama3.3 # Latest Llama\nollama pull llama3.2 # Llama 3.2\nollama pull qwen3 # Qwen 3\nollama pull qwen2.5-coder # Coding-focused\nollama pull deepseek-coder # DeepSeek coder\nollama pull granite3.3 # IBM Granite\nollama pull mistral # Mistral\nollama pull gemma3 # Google Gemma\nollama pull phi3 # Microsoft Phi\nollama pull codellama # Code Llama\n\n# List available Ollama models\nollama list\n\n# Configure remote Ollama server\nmcp-cli provider set ollama api_base http://remote-server:11434\n```\n\n### Cloud Provider Configuration\n\nTo use cloud providers with advanced models, configure API keys:\n\n```bash\n# Configure OpenAI (for GPT-5, GPT-4, O3 models)\nmcp-cli provider set openai api_key sk-your-key-here\n\n# Configure Anthropic (for Claude 4, Claude 3.5)\nmcp-cli provider set anthropic api_key sk-ant-your-key-here\n\n# Configure Azure OpenAI (for enterprise GPT-5)\nmcp-cli provider set azure_openai api_key sk-your-key-here\nmcp-cli provider set azure_openai api_base https://your-resource.openai.azure.com\n\n# Configure other providers\nmcp-cli provider set gemini api_key your-gemini-key\nmcp-cli provider set groq api_key your-groq-key\n\n# Test configuration\nmcp-cli provider diagnostic openai\nmcp-cli provider diagnostic anthropic\n```\n\n### Custom OpenAI-Compatible Providers\n\nMCP CLI supports adding custom OpenAI-compatible providers (LocalAI, custom proxies, etc.):\n\n```bash\n# Add a custom provider (persisted across sessions)\nmcp-cli provider add localai http://localhost:8080/v1 gpt-4 gpt-3.5-turbo\nmcp-cli provider add myproxy https://proxy.example.com/v1 custom-model-1 custom-model-2\n\n# Set API key via environment variable (never stored in config)\nexport LOCALAI_API_KEY=your-api-key\nexport MYPROXY_API_KEY=your-api-key\n\n# List custom providers\nmcp-cli provider custom\n\n# Use custom provider\nmcp-cli --provider localai --server sqlite\nmcp-cli --provider myproxy --model custom-model-1 --server sqlite\n\n# Remove custom provider\nmcp-cli provider remove localai\n\n# Runtime provider (session-only, not persisted)\nmcp-cli --provider temp-ai --api-base https://api.temp.com/v1 --api-key test-key --server sqlite\n```\n\n**Security Note**: API keys are NEVER stored in configuration files. Use environment variables following the pattern `{PROVIDER_NAME}_API_KEY` or pass via `--api-key` for session-only use.\n\n### Manual Configuration\n\nThe `chuk_llm` library configuration in `~/.chuk_llm/config.yaml`:\n\n```yaml\nollama:\n api_base: http://localhost:11434\n default_model: gpt-oss\n\nopenai:\n api_base: https://api.openai.com/v1\n default_model: gpt-5\n\nanthropic:\n api_base: https://api.anthropic.com\n default_model: claude-4-1-opus\n\nazure_openai:\n api_base: https://your-resource.openai.azure.com\n default_model: gpt-5\n\ngemini:\n api_base: https://generativelanguage.googleapis.com\n default_model: gemini-2.0-flash\n\ngroq:\n api_base: https://api.groq.com\n default_model: llama-3.1-70b\n```\n\nAPI keys (if using cloud providers) in `~/.chuk_llm/.env`:\n\n```bash\nOPENAI_API_KEY=sk-your-key-here\nANTHROPIC_API_KEY=sk-ant-your-key-here\nAZURE_OPENAI_API_KEY=sk-your-azure-key-here\nGEMINI_API_KEY=your-gemini-key\nGROQ_API_KEY=your-groq-key\n```\n\n## \ud83d\udcc2 Server Configuration\n\nMCP CLI supports two types of server configurations:\n\n1. **Project Servers** (`server_config.json`): Shared project-level configurations\n2. **User Servers** (`~/.mcp-cli/preferences.json`): Personal runtime-added servers that persist across sessions\n\n### Project Configuration\n\nCreate a `server_config.json` file with your MCP server configurations:\n\n```json\n{\n \"mcpServers\": {\n \"sqlite\": {\n \"command\": \"python\",\n \"args\": [\"-m\", \"mcp_server.sqlite_server\"],\n \"env\": {\n \"DATABASE_PATH\": \"database.db\"\n }\n },\n \"filesystem\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@modelcontextprotocol/server-filesystem\", \"/path/to/allowed/files\"],\n \"env\": {}\n },\n \"brave-search\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@modelcontextprotocol/server-brave-search\"],\n \"env\": {\n \"BRAVE_API_KEY\": \"your-brave-api-key\"\n }\n }\n }\n}\n```\n\n### Runtime Server Management\n\nAdd servers dynamically during runtime without editing configuration files:\n\n```bash\n# Add STDIO servers (most common)\nmcp-cli\n> /server add sqlite stdio uvx mcp-server-sqlite --db-path mydata.db\n> /server add playwright stdio npx @playwright/mcp@latest\n> /server add time stdio uvx mcp-server-time\n\n# Add HTTP servers with authentication\n> /server add github --transport http --header \"Authorization: Bearer ghp_token\" -- https://api.github.com/mcp\n> /server add myapi --transport http --env API_KEY=secret -- https://api.example.com/mcp\n\n# Add SSE (Server-Sent Events) servers\n> /server add events --transport sse -- https://events.example.com/sse\n\n# Manage servers\n> /server list # Show all servers\n> /server disable sqlite # Temporarily disable\n> /server enable sqlite # Re-enable\n> /server remove myapi # Remove user-added server\n```\n\n**Key Points:**\n- User-added servers persist in `~/.mcp-cli/preferences.json`\n- Survive application restarts\n- Can be enabled/disabled without removal\n- Support STDIO, HTTP, and SSE transports\n- Environment variables and headers for authentication\n\n## \ud83d\udcc8 Advanced Usage Examples\n\n### Reasoning Model Comparison\n\n```bash\n# Compare reasoning across different models\n> /provider ollama\n> /model gpt-oss\n> Think through this problem step by step: If a train leaves New York at 3 PM...\n[See the complete thinking process with gpt-oss]\n\n> /provider openai\n> /model gpt-5\n> Think through this problem step by step: If a train leaves New York at 3 PM...\n[See GPT-5's reasoning approach]\n\n> /provider anthropic\n> /model claude-4-1-opus\n> Think through this problem step by step: If a train leaves New York at 3 PM...\n[See Claude 4's analytical process]\n```\n\n### Local-First Workflow with Reasoning\n\n```bash\n# Start with default Ollama/gpt-oss (no API key needed)\nmcp-cli chat --server sqlite\n\n# Use reasoning model for complex problems\n> Think through this database optimization problem step by step\n[gpt-oss shows its complete thinking process before answering]\n\n# Try different local models for different tasks\n> /model llama3.3 # General purpose\n> /model qwen2.5-coder # For coding tasks\n> /model deepseek-coder # Alternative coding model\n> /model granite3.3 # IBM's model\n> /model gpt-oss # Back to reasoning model\n\n# Switch to cloud when needed (requires API keys)\n> /provider openai\n> /model gpt-5\n> Complex enterprise architecture design...\n\n> /provider anthropic\n> /model claude-4-1-opus\n> Detailed strategic analysis...\n\n> /provider ollama\n> /model gpt-oss\n> Continue with local processing...\n```\n\n### Multi-Provider Workflow\n\n```bash\n# Start with local reasoning (default, no API key)\nmcp-cli chat --server sqlite\n\n# Compare responses across providers\n> /provider ollama\n> What's the best way to optimize this SQL query?\n\n> /provider openai gpt-5 # Requires API key\n> What's the best way to optimize this SQL query?\n\n> /provider anthropic claude-4-sonnet # Requires API key\n> What's the best way to optimize this SQL query?\n\n# Use each provider's strengths\n> /provider ollama gpt-oss # Local reasoning, privacy\n> /provider openai gpt-5 # Advanced reasoning\n> /provider anthropic claude-4-1-opus # Deep analysis\n> /provider groq llama-3.1-70b # Ultra-fast responses\n```\n\n### Complex Tool Workflows with Reasoning\n\n```bash\n# Use reasoning model for complex database tasks\n> /model gpt-oss\n> I need to analyze our database performance. Think through what we should check first.\n[gpt-oss shows thinking: \"First, I should check the table structure, then indexes, then query patterns...\"]\n[Tool: list_tables] \u2192 products, customers, orders\n\n> Now analyze the indexes and suggest optimizations\n[gpt-oss thinks through index analysis]\n[Tool: describe_table] \u2192 Shows current indexes\n[Tool: read_query] \u2192 Analyzes query patterns\n\n> Create an optimization plan based on your analysis\n[Complete reasoning process followed by specific recommendations]\n```\n\n### Automation and Scripting\n\n```bash\n# Batch processing with different models\nfor file in data/*.csv; do\n # Use reasoning model for analysis\n mcp-cli cmd --server sqlite \\\n --model gpt-oss \\\n --prompt \"Analyze this data and think through patterns\" \\\n --input \"$file\" \\\n --output \"analysis/$(basename \"$file\" .csv)_reasoning.txt\"\n \n # Use coding model for generating scripts\n mcp-cli cmd --server sqlite \\\n --model qwen2.5-coder \\\n --prompt \"Generate Python code to process this data\" \\\n --input \"$file\" \\\n --output \"scripts/$(basename \"$file\" .csv)_script.py\"\ndone\n\n# Pipeline with reasoning\ncat complex_problem.txt | \\\n mcp-cli cmd --model gpt-oss --prompt \"Think through this step by step\" --input - | \\\n mcp-cli cmd --model llama3.3 --prompt \"Summarize the key points\" --input - > solution.txt\n```\n\n### Performance Monitoring\n\n```bash\n# Check provider and model performance\n> /provider diagnostic\nProvider Diagnostics\nProvider | Status | Response Time | Features | Models\nollama | \u2705 Ready | 56ms | \ud83d\udce1\ud83d\udd27 | gpt-oss, llama3.3, qwen3, ...\nopenai | \u2705 Ready | 234ms | \ud83d\udce1\ud83d\udd27\ud83d\udc41\ufe0f | gpt-5, gpt-4o, o3, ...\nanthropic | \u2705 Ready | 187ms | \ud83d\udce1\ud83d\udd27 | claude-4-1-opus, claude-4-sonnet, ...\nazure_openai | \u2705 Ready | 198ms | \ud83d\udce1\ud83d\udd27\ud83d\udc41\ufe0f | gpt-5, gpt-4o, ...\ngemini | \u2705 Ready | 156ms | \ud83d\udce1\ud83d\udd27\ud83d\udc41\ufe0f | gemini-2.0-flash, ...\ngroq | \u2705 Ready | 45ms | \ud83d\udce1\ud83d\udd27 | llama-3.1-70b, ...\n\n# Check available models\n> /models\nModels for ollama (Current Provider)\nModel | Status\ngpt-oss | Current & Default (Reasoning)\nllama3.3 | Available\nllama3.2 | Available\nqwen2.5-coder | Available\ndeepseek-coder | Available\ngranite3.3 | Available\n... and 6 more\n\n# Monitor tool execution with reasoning\n> /verbose\n> /model gpt-oss\n> Analyze the database and optimize the slowest queries\n[Shows complete thinking process]\n[Tool execution with timing]\n```\n\n## \ud83d\udd0d Troubleshooting\n\n### Common Issues\n\n1. **Ollama not running** (default provider):\n ```bash\n # Start Ollama service\n ollama serve\n \n # Or check if it's running\n curl http://localhost:11434/api/tags\n ```\n\n2. **Model not found**:\n ```bash\n # For Ollama (default), pull the model first\n ollama pull gpt-oss # Reasoning model\n ollama pull llama3.3 # Latest Llama\n ollama pull qwen2.5-coder # Coding model\n \n # List available models\n ollama list\n \n # For cloud providers, check supported models\n mcp-cli models openai # Shows GPT-5, GPT-4, O3 models\n mcp-cli models anthropic # Shows Claude 4, Claude 3.5 models\n ```\n\n3. **Provider not found or API key missing**:\n ```bash\n # Check available providers\n mcp-cli provider list\n \n # For cloud providers, set API keys\n mcp-cli provider set openai api_key sk-your-key\n mcp-cli provider set anthropic api_key sk-ant-your-key\n \n # Test connection\n mcp-cli provider diagnostic openai\n ```\n\n4. **Connection issues with Ollama**:\n ```bash\n # Check Ollama is running\n ollama list\n \n # Test connection\n mcp-cli provider diagnostic ollama\n \n # Configure custom endpoint if needed\n mcp-cli provider set ollama api_base http://localhost:11434\n ```\n\n### Debug Mode\n\nEnable verbose logging for troubleshooting:\n\n```bash\nmcp-cli --verbose chat --server sqlite\nmcp-cli --log-level DEBUG interactive --server sqlite\n```\n\n## \ud83d\udd12 Security Considerations\n\n- **Local by Default**: Ollama with gpt-oss runs locally, keeping your data private\n- **API Keys**: Only needed for cloud providers (OpenAI, Anthropic, etc.), stored securely\n- **File Access**: Filesystem access can be disabled with `--disable-filesystem`\n- **Tool Validation**: All tool calls are validated before execution\n- **Timeout Protection**: Configurable timeouts prevent hanging operations\n- **Server Isolation**: Each server runs in its own process\n\n## \ud83d\ude80 Performance Features\n\n- **Local Processing**: Default Ollama provider minimizes latency\n- **Reasoning Visibility**: See AI thinking process with gpt-oss, GPT-5, Claude 4\n- **Concurrent Tool Execution**: Multiple tools can run simultaneously\n- **Streaming Responses**: Real-time response generation\n- **Connection Pooling**: Efficient reuse of client connections\n- **Caching**: Tool metadata and provider configurations are cached\n- **Async Architecture**: Non-blocking operations throughout\n\n## \ud83d\udce6 Dependencies\n\nCore dependencies are organized into feature groups:\n\n- **cli**: Terminal UI and command framework (Rich, Typer, chuk-term)\n- **dev**: Development tools, testing utilities, linting\n- **chuk-tool-processor**: Core tool execution and MCP communication\n- **chuk-llm**: Unified LLM provider management with 200+ auto-generated functions\n- **chuk-term**: Enhanced terminal UI with themes, prompts, and cross-platform support\n\nInstall with specific features:\n```bash\npip install \"mcp-cli[cli]\" # Basic CLI features\npip install \"mcp-cli[cli,dev]\" # CLI with development tools\n```\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Development Setup\n\n```bash\ngit clone https://github.com/chrishayuk/mcp-cli\ncd mcp-cli\npip install -e \".[cli,dev]\"\npre-commit install\n```\n\n### Demo Scripts\n\nExplore the capabilities of MCP CLI:\n\n```bash\n# Custom Provider Management Demos\n\n# Interactive walkthrough demo (educational)\nuv run examples/custom_provider_demo.py\n\n# Working demo with actual inference (requires OPENAI_API_KEY)\nuv run examples/custom_provider_working_demo.py\n\n# Simple shell script demo (requires OPENAI_API_KEY)\nbash examples/custom_provider_simple_demo.sh\n\n# Terminal management features (chuk-term)\nuv run examples/ui_terminal_demo.py\n\n# Output system with themes (chuk-term)\nuv run examples/ui_output_demo.py\n\n# Streaming UI capabilities (chuk-term)\nuv run examples/ui_streaming_demo.py\n```\n\n### Running Tests\n\n```bash\npytest\npytest --cov=mcp_cli --cov-report=html\n```\n\n## \ud83d\udcdc License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\ude4f Acknowledgments\n\n- **[CHUK Tool Processor](https://github.com/chrishayuk/chuk-tool-processor)** - Async-native tool execution\n- **[CHUK-LLM](https://github.com/chrishayuk/chuk-llm)** - Unified LLM provider management with GPT-5, Claude 4, and reasoning model support\n- **[CHUK-Term](https://github.com/chrishayuk/chuk-term)** - Enhanced terminal UI with themes and cross-platform support\n- **[Rich](https://github.com/Textualize/rich)** - Beautiful terminal formatting\n- **[Typer](https://typer.tiangolo.com/)** - CLI framework\n- **[Prompt Toolkit](https://github.com/prompt-toolkit/python-prompt-toolkit)** - Interactive input\n\n## \ud83d\udd17 Related Projects\n\n- **[Model Context Protocol](https://modelcontextprotocol.io/)** - Core protocol specification\n- **[MCP Servers](https://github.com/modelcontextprotocol/servers)** - Official MCP server implementations\n- **[CHUK Tool Processor](https://github.com/chrishayuk/chuk-tool-processor)** - Tool execution engine\n- **[CHUK-LLM](https://github.com/chrishayuk/chuk-llm)** - LLM provider abstraction with GPT-5, Claude 4, O3 series support\n- **[CHUK-Term](https://github.com/chrishayuk/chuk-term)** - Terminal UI library with themes and cross-platform support\n",
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