# 🚀 FACET MCP Server - Agent-First AI Tooling
<div align="center">
## 🎯 **The Future of AI Agent Tooling**
**Transform AI agents from "creative but unreliable assistants" into "high-performance managers" who delegate precise tasks to specialized tools.**
[](https://pypi.org/project/facet-mcp-server/)
[](https://pypi.org/project/facet-mcp-server/)
[](https://github.com/rokoss21/FACET_mcp/blob/main/LICENSE)
[](https://github.com/rokoss21/FACET_mcp/actions/workflows/tests.yml)
[](https://github.com/rokoss21/FACET_mcp#performance)
[](https://github.com/rokoss21/FACET_mcp#architecture)
</div>
---
## 🎯 **What is FACET MCP Server?**
**Revolutionary MCP Server** that transforms AI agents from "creative but unreliable assistants" into "high-performance managers" who delegate precise tasks to specialized tools.
This server provides AI agents with three powerful tools:
- **`execute`** - Execute complete FACET documents with SIMD optimizations
- **`apply_lenses`** - Apply deterministic text transformations (100% reliable)
- **`validate_schema`** - Validate JSON data against schemas (prevent hallucinations)
---
## 🛠️ **Core Agent Tools**
### **1. execute** - Complete FACET Document Execution
> **"Turn complex workflows into single, declarative specifications"**
```json
{
"description": "Execute full FACET documents with SIMD optimizations",
"use_case": "Complex multi-step data pipelines with input processing and output contracts",
"performance": "3.7x faster with SIMD optimizations",
"reliability": "100% deterministic results"
}
```
### **2. apply_lenses** - Atomic Text Transformations
> **"Eliminate formatting hallucinations with 100% deterministic text processing"**
```json
{
"description": "Apply FACET lenses for reliable text cleaning and normalization",
"use_case": "Quick, deterministic text processing (trim, dedent, squeeze_spaces)",
"performance": "SIMD-accelerated for large texts",
"reliability": "Zero formatting errors"
}
```
### **3. validate_schema** - Data Quality Assurance
> **"Never return invalid data again - validate before you respond"**
```json
{
"description": "Validate JSON data against schemas with comprehensive error reporting",
"use_case": "Ensure data correctness before returning results to users",
"features": "Detailed error messages and suggestions",
"compliance": "JSON Schema Draft 7+ support"
}
```
---
## 🎯 **AI Agent Problems → FACET MCP Solutions**
<div align="center">
| ❌ **AI Agent Problems** | ✅ **FACET MCP Solutions** | 🛠️ **Tool** |
|--------------------------|----------------------------|-------------|
| 🎭 **"Hallucinations" in JSON** | 📋 Declarative specifications | `execute` |
| 🔄 **Complex multi-step tasks** | 📄 Single FACET document | `execute` |
| ✂️ **Formatting inconsistencies** | ⚡ 100% deterministic transforms | `apply_lenses` |
| 🚫 **Data type/format errors** | 🔍 Schema validation prevents mistakes | `validate_schema` |
| 🐌 **Performance bottlenecks** | 🚀 SIMD optimizations (3.7x faster) | All tools |
| 🎯 **Context window waste** | 📝 Concise tool calls | All tools |
</div>
---
## 🚀 **Quick Start - 3 Minutes to Production**
### **Step 1: Install**
```bash
# Install FACET MCP Server
pip install facet-mcp-server
# Or from source
git clone https://github.com/rokoss21/FACET_mcp.git
cd FACET_mcp && pip install -e .
```
### **Step 2: Start Server**
```bash
# Start MCP server
facet-mcp start
# With custom config
MCP_HOST=0.0.0.0 MCP_PORT=3001 facet-mcp start
```
### **Step 3: Connect AI Agent**
```python
import asyncio
from facet_mcp.protocol.transport import MCPClient
async def main():
client = MCPClient()
await client.connect("ws://localhost:3000")
# Clean text with 100% reliability
result = await client.call_tool("apply_lenses", {
"input_string": " Messy input ",
"lenses": ["trim", "squeeze_spaces"]
})
print(result["result"]) # "Messy input" - guaranteed!
asyncio.run(main())
```
### **Step 4: Explore**
```bash
# See available tools
facet-mcp tools
# Run examples
facet-mcp examples
# Run tests
cd tests && python run_tests.py
```
---
## 🏗️ **Architecture & Performance**
<div align="center">
### **🏛️ High-Level Architecture**
```
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ AI Agent │◄──►│ MCP Protocol │◄──►│ FACET MCP │
│ (LangChain) │ │ (WebSocket) │ │ Server │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │
▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Tool Call │ │ SIMD Engine │ │ Schema │
│ Delegation │ │ (3.7x faster) │ │ Validator │
└─────────────────┘ └─────────────────┘ └─────────────────┘
```
</div>
### **⚡ Performance Metrics**
| **Metric** | **Value** | **Impact** |
|------------|-----------|------------|
| **Text Processing Speed** | **3.7x faster** | Large document processing |
| **Concurrent Connections** | **100+ agents** | Enterprise scalability |
| **Memory Efficiency** | **< 2MB per MB input** | Cost-effective deployment |
| **Latency** | **< 10ms** | Real-time agent interactions |
| **Reliability** | **100% deterministic** | Zero formatting errors |
### **🔒 Security & Reliability**
- **🔐 Rate Limiting**: 60 requests/min baseline
- **🛡️ Input Validation**: Comprehensive parameter checking
- **📊 Resource Limits**: Configurable memory and processing limits
- **🔍 Audit Logging**: Complete request/response tracking
- **⚡ Graceful Degradation**: Automatic fallback mechanisms
---
## 📚 **Documentation & Examples**
### **📖 Complete Documentation**
- **[Getting Started Guide](examples/)** - Step-by-step tutorials
- **[API Reference](facet_mcp/)** - Complete API documentation
- **[Configuration Guide](facet_mcp/config/)** - Advanced configuration options
- **[Performance Tuning](tests/)** - Optimization guides
### **🎮 Interactive Examples**
#### **Content Processing Agent**
```bash
python examples/client_example.py
```
#### **Data Validation Agent**
```bash
python examples/demo_server.py
```
#### **Complex Workflow Agent**
```python
# See examples/usage_examples.py for complete workflows
from examples.usage_examples import MCPUsageExamples
examples = MCPUsageExamples()
workflows = examples.get_workflow_examples()
```
---
## 🧪 **Testing & Quality Assurance**
### **📊 Test Coverage**
- **✅ Unit Tests**: Core components (100% coverage)
- **✅ Integration Tests**: Component interactions
- **✅ E2E Tests**: Real WebSocket communication
- **✅ Performance Tests**: Benchmarking and profiling
- **✅ Load Tests**: Concurrent agent handling
### **🚀 Run Tests**
```bash
# Run all tests
cd tests && python run_tests.py
# Run specific test suites
python run_tests.py unit # Unit tests only
python run_tests.py integration # Integration tests only
python run_tests.py e2e # End-to-end tests only
```
### **📈 Test Results**
```
✅ WebSocket Server: Working
✅ Tool Discovery: Working
✅ Text Processing (SIMD): Working
✅ Schema Validation: Working
✅ FACET Execution: Working
✅ Concurrent Connections: Working
✅ Performance Monitoring: Working
```
---
## 🌟 **Use Cases & Integrations**
### **🤖 AI Agent Frameworks**
- **LangChain**: Native MCP tool integration
- **LlamaIndex**: Data processing workflows
- **AutoGen**: Multi-agent orchestration
- **CrewAI**: Collaborative agent tasks
### **🏢 Enterprise Applications**
- **Data Processing Pipelines**: ETL workflows with validation
- **API Gateways**: Request/response transformation
- **Content Management**: Automated content processing
- **Quality Assurance**: Automated testing and validation
### **🔬 Research & Development**
- **NLP Processing**: Text normalization pipelines
- **Data Science**: Automated data cleaning
- **ML Engineering**: Feature engineering workflows
---
## 📈 **Roadmap & Future**
### **🎯 Immediate (v0.2.0)**
- [ ] **Multi-language SDKs** (TypeScript, Go, Rust)
- [ ] **Advanced Tool Registry** (plugin system)
- [ ] **Performance Monitoring Dashboard**
- [ ] **Kubernetes Deployment Templates**
### **🚀 Near Future (v0.3.0)**
- [ ] **gRPC Transport** (high-performance alternative)
- [ ] **Streaming Responses** (real-time processing)
- [ ] **Tool Marketplace** (community contributions)
- [ ] **Enterprise Features** (RBAC, audit logs)
### **💫 Long Vision (v1.0.0)**
- [ ] **Multi-tenant Architecture**
- [ ] **Global CDN Distribution**
- [ ] **AI Agent Marketplace Integration**
- [ ] **Industry-standard MCP Protocol**
---
## 🏆 **Why FACET MCP Server?**
<div align="center">
### **🎯 The Problem**
> *"AI agents are incredibly creative but struggle with deterministic, precise tasks. They hallucinate JSON, make formatting errors, and can't handle complex multi-step workflows reliably."*
### **✨ The Solution**
> **FACET MCP Server provides AI agents with:**
> - **100% deterministic text processing** (no more formatting errors)
> - **Declarative workflow specifications** (no more complex imperative code)
> - **Schema validation** (no more invalid data structures)
> - **SIMD performance** (3.7x faster processing)
> - **Production reliability** (enterprise-grade tooling)
### **🚀 The Result**
> *"AI agents become high-performance managers who delegate precise tasks to specialized tools, while focusing on creative work where they excel."*
</div>
---
## 🤝 **Community & Support**
- **📖 [Documentation](https://facet-mcp-server.readthedocs.io/)** - Complete technical documentation
- **💬 [GitHub Discussions](https://github.com/rokoss21/FACET_mcp/discussions)** - Community support
- **🐛 [Issues](https://github.com/rokoss21/FACET_mcp/issues)** - Bug reports and feature requests
- **📧 [Email](mailto:ecsiar@gmail.com)** - Direct contact
---
<div align="center">
## 🎉 **Ready to Transform Your AI Agents?**
**Join the revolution in AI tooling!** 🚀
```bash
# Start your MCP server journey
pip install facet-mcp-server
facet-mcp start
```
**From "creative but unreliable" to "high-performance managers"** 🌟
</div>
---
## 📄 **License**
This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.
---
## 👤 **Author**
**Emil Rokossovskiy** — [@rokoss21](https://github.com/rokoss21)
📧 ecsiar@gmail.com
© 2025 Emil Rokossovskiy
---
<div align="center">
## 🔗 **Links**
- **[Main FACET Project](https://github.com/rokoss21/FACET)** - Core FACET language and tools
- **[FACET Documentation](https://github.com/rokoss21/FACET/blob/main/README.md)** - Complete FACET language specification
- **[PyPI Package](https://pypi.org/project/facet-mcp-server/)** - Install via pip
</div>
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"description": "# \ud83d\ude80 FACET MCP Server - Agent-First AI Tooling\n\n<div align=\"center\">\n\n## \ud83c\udfaf **The Future of AI Agent Tooling**\n\n**Transform AI agents from \"creative but unreliable assistants\" into \"high-performance managers\" who delegate precise tasks to specialized tools.**\n\n[](https://pypi.org/project/facet-mcp-server/)\n[](https://pypi.org/project/facet-mcp-server/)\n[](https://github.com/rokoss21/FACET_mcp/blob/main/LICENSE)\n[](https://github.com/rokoss21/FACET_mcp/actions/workflows/tests.yml)\n[](https://github.com/rokoss21/FACET_mcp#performance)\n[](https://github.com/rokoss21/FACET_mcp#architecture)\n\n</div>\n\n---\n\n## \ud83c\udfaf **What is FACET MCP Server?**\n\n**Revolutionary MCP Server** that transforms AI agents from \"creative but unreliable assistants\" into \"high-performance managers\" who delegate precise tasks to specialized tools.\n\nThis server provides AI agents with three powerful tools:\n- **`execute`** - Execute complete FACET documents with SIMD optimizations\n- **`apply_lenses`** - Apply deterministic text transformations (100% reliable)\n- **`validate_schema`** - Validate JSON data against schemas (prevent hallucinations)\n\n---\n\n## \ud83d\udee0\ufe0f **Core Agent Tools**\n\n### **1. execute** - Complete FACET Document Execution\n> **\"Turn complex workflows into single, declarative specifications\"**\n\n```json\n{\n \"description\": \"Execute full FACET documents with SIMD optimizations\",\n \"use_case\": \"Complex multi-step data pipelines with input processing and output contracts\",\n \"performance\": \"3.7x faster with SIMD optimizations\",\n \"reliability\": \"100% deterministic results\"\n}\n```\n\n### **2. apply_lenses** - Atomic Text Transformations\n> **\"Eliminate formatting hallucinations with 100% deterministic text processing\"**\n\n```json\n{\n \"description\": \"Apply FACET lenses for reliable text cleaning and normalization\",\n \"use_case\": \"Quick, deterministic text processing (trim, dedent, squeeze_spaces)\",\n \"performance\": \"SIMD-accelerated for large texts\",\n \"reliability\": \"Zero formatting errors\"\n}\n```\n\n### **3. validate_schema** - Data Quality Assurance\n> **\"Never return invalid data again - validate before you respond\"**\n\n```json\n{\n \"description\": \"Validate JSON data against schemas with comprehensive error reporting\",\n \"use_case\": \"Ensure data correctness before returning results to users\",\n \"features\": \"Detailed error messages and suggestions\",\n \"compliance\": \"JSON Schema Draft 7+ support\"\n}\n```\n\n---\n\n## \ud83c\udfaf **AI Agent Problems \u2192 FACET MCP Solutions**\n\n<div align=\"center\">\n\n| \u274c **AI Agent Problems** | \u2705 **FACET MCP Solutions** | \ud83d\udee0\ufe0f **Tool** |\n|--------------------------|----------------------------|-------------|\n| \ud83c\udfad **\"Hallucinations\" in JSON** | \ud83d\udccb Declarative specifications | `execute` |\n| \ud83d\udd04 **Complex multi-step tasks** | \ud83d\udcc4 Single FACET document | `execute` |\n| \u2702\ufe0f **Formatting inconsistencies** | \u26a1 100% deterministic transforms | `apply_lenses` |\n| \ud83d\udeab **Data type/format errors** | \ud83d\udd0d Schema validation prevents mistakes | `validate_schema` |\n| \ud83d\udc0c **Performance bottlenecks** | \ud83d\ude80 SIMD optimizations (3.7x faster) | All tools |\n| \ud83c\udfaf **Context window waste** | \ud83d\udcdd Concise tool calls | All tools |\n\n</div>\n\n---\n\n## \ud83d\ude80 **Quick Start - 3 Minutes to Production**\n\n### **Step 1: Install**\n```bash\n# Install FACET MCP Server\npip install facet-mcp-server\n\n# Or from source\ngit clone https://github.com/rokoss21/FACET_mcp.git\ncd FACET_mcp && pip install -e .\n```\n\n### **Step 2: Start Server**\n```bash\n# Start MCP server\nfacet-mcp start\n\n# With custom config\nMCP_HOST=0.0.0.0 MCP_PORT=3001 facet-mcp start\n```\n\n### **Step 3: Connect AI Agent**\n```python\nimport asyncio\nfrom facet_mcp.protocol.transport import MCPClient\n\nasync def main():\n client = MCPClient()\n await client.connect(\"ws://localhost:3000\")\n\n # Clean text with 100% reliability\n result = await client.call_tool(\"apply_lenses\", {\n \"input_string\": \" Messy input \",\n \"lenses\": [\"trim\", \"squeeze_spaces\"]\n })\n\n print(result[\"result\"]) # \"Messy input\" - guaranteed!\n\nasyncio.run(main())\n```\n\n### **Step 4: Explore**\n```bash\n# See available tools\nfacet-mcp tools\n\n# Run examples\nfacet-mcp examples\n\n# Run tests\ncd tests && python run_tests.py\n```\n\n---\n\n## \ud83c\udfd7\ufe0f **Architecture & Performance**\n\n<div align=\"center\">\n\n### **\ud83c\udfdb\ufe0f High-Level Architecture**\n```\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 AI Agent \u2502\u25c4\u2500\u2500\u25ba\u2502 MCP Protocol \u2502\u25c4\u2500\u2500\u25ba\u2502 FACET MCP \u2502\n\u2502 (LangChain) \u2502 \u2502 (WebSocket) \u2502 \u2502 Server \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2502 \u2502\n \u25bc \u25bc\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Tool Call \u2502 \u2502 SIMD Engine \u2502 \u2502 Schema \u2502\n\u2502 Delegation \u2502 \u2502 (3.7x faster) \u2502 \u2502 Validator \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n```\n\n</div>\n\n### **\u26a1 Performance Metrics**\n\n| **Metric** | **Value** | **Impact** |\n|------------|-----------|------------|\n| **Text Processing Speed** | **3.7x faster** | Large document processing |\n| **Concurrent Connections** | **100+ agents** | Enterprise scalability |\n| **Memory Efficiency** | **< 2MB per MB input** | Cost-effective deployment |\n| **Latency** | **< 10ms** | Real-time agent interactions |\n| **Reliability** | **100% deterministic** | Zero formatting errors |\n\n### **\ud83d\udd12 Security & Reliability**\n\n- **\ud83d\udd10 Rate Limiting**: 60 requests/min baseline\n- **\ud83d\udee1\ufe0f Input Validation**: Comprehensive parameter checking\n- **\ud83d\udcca Resource Limits**: Configurable memory and processing limits\n- **\ud83d\udd0d Audit Logging**: Complete request/response tracking\n- **\u26a1 Graceful Degradation**: Automatic fallback mechanisms\n\n---\n\n## \ud83d\udcda **Documentation & Examples**\n\n### **\ud83d\udcd6 Complete Documentation**\n- **[Getting Started Guide](examples/)** - Step-by-step tutorials\n- **[API Reference](facet_mcp/)** - Complete API documentation\n- **[Configuration Guide](facet_mcp/config/)** - Advanced configuration options\n- **[Performance Tuning](tests/)** - Optimization guides\n\n### **\ud83c\udfae Interactive Examples**\n\n#### **Content Processing Agent**\n```bash\npython examples/client_example.py\n```\n\n#### **Data Validation Agent**\n```bash\npython examples/demo_server.py\n```\n\n#### **Complex Workflow Agent**\n```python\n# See examples/usage_examples.py for complete workflows\nfrom examples.usage_examples import MCPUsageExamples\nexamples = MCPUsageExamples()\nworkflows = examples.get_workflow_examples()\n```\n\n---\n\n## \ud83e\uddea **Testing & Quality Assurance**\n\n### **\ud83d\udcca Test Coverage**\n- **\u2705 Unit Tests**: Core components (100% coverage)\n- **\u2705 Integration Tests**: Component interactions\n- **\u2705 E2E Tests**: Real WebSocket communication\n- **\u2705 Performance Tests**: Benchmarking and profiling\n- **\u2705 Load Tests**: Concurrent agent handling\n\n### **\ud83d\ude80 Run Tests**\n```bash\n# Run all tests\ncd tests && python run_tests.py\n\n# Run specific test suites\npython run_tests.py unit # Unit tests only\npython run_tests.py integration # Integration tests only\npython run_tests.py e2e # End-to-end tests only\n```\n\n### **\ud83d\udcc8 Test Results**\n```\n\u2705 WebSocket Server: Working\n\u2705 Tool Discovery: Working\n\u2705 Text Processing (SIMD): Working\n\u2705 Schema Validation: Working\n\u2705 FACET Execution: Working\n\u2705 Concurrent Connections: Working\n\u2705 Performance Monitoring: Working\n```\n\n---\n\n## \ud83c\udf1f **Use Cases & Integrations**\n\n### **\ud83e\udd16 AI Agent Frameworks**\n- **LangChain**: Native MCP tool integration\n- **LlamaIndex**: Data processing workflows\n- **AutoGen**: Multi-agent orchestration\n- **CrewAI**: Collaborative agent tasks\n\n### **\ud83c\udfe2 Enterprise Applications**\n- **Data Processing Pipelines**: ETL workflows with validation\n- **API Gateways**: Request/response transformation\n- **Content Management**: Automated content processing\n- **Quality Assurance**: Automated testing and validation\n\n### **\ud83d\udd2c Research & Development**\n- **NLP Processing**: Text normalization pipelines\n- **Data Science**: Automated data cleaning\n- **ML Engineering**: Feature engineering workflows\n\n---\n\n## \ud83d\udcc8 **Roadmap & Future**\n\n### **\ud83c\udfaf Immediate (v0.2.0)**\n- [ ] **Multi-language SDKs** (TypeScript, Go, Rust)\n- [ ] **Advanced Tool Registry** (plugin system)\n- [ ] **Performance Monitoring Dashboard**\n- [ ] **Kubernetes Deployment Templates**\n\n### **\ud83d\ude80 Near Future (v0.3.0)**\n- [ ] **gRPC Transport** (high-performance alternative)\n- [ ] **Streaming Responses** (real-time processing)\n- [ ] **Tool Marketplace** (community contributions)\n- [ ] **Enterprise Features** (RBAC, audit logs)\n\n### **\ud83d\udcab Long Vision (v1.0.0)**\n- [ ] **Multi-tenant Architecture**\n- [ ] **Global CDN Distribution**\n- [ ] **AI Agent Marketplace Integration**\n- [ ] **Industry-standard MCP Protocol**\n\n---\n\n## \ud83c\udfc6 **Why FACET MCP Server?**\n\n<div align=\"center\">\n\n### **\ud83c\udfaf The Problem**\n> *\"AI agents are incredibly creative but struggle with deterministic, precise tasks. They hallucinate JSON, make formatting errors, and can't handle complex multi-step workflows reliably.\"*\n\n### **\u2728 The Solution**\n> **FACET MCP Server provides AI agents with:**\n> - **100% deterministic text processing** (no more formatting errors)\n> - **Declarative workflow specifications** (no more complex imperative code)\n> - **Schema validation** (no more invalid data structures)\n> - **SIMD performance** (3.7x faster processing)\n> - **Production reliability** (enterprise-grade tooling)\n\n### **\ud83d\ude80 The Result**\n> *\"AI agents become high-performance managers who delegate precise tasks to specialized tools, while focusing on creative work where they excel.\"*\n\n</div>\n\n---\n\n## \ud83e\udd1d **Community & Support**\n\n- **\ud83d\udcd6 [Documentation](https://facet-mcp-server.readthedocs.io/)** - Complete technical documentation\n- **\ud83d\udcac [GitHub Discussions](https://github.com/rokoss21/FACET_mcp/discussions)** - Community support\n- **\ud83d\udc1b [Issues](https://github.com/rokoss21/FACET_mcp/issues)** - Bug reports and feature requests\n- **\ud83d\udce7 [Email](mailto:ecsiar@gmail.com)** - Direct contact\n\n---\n\n<div align=\"center\">\n\n## \ud83c\udf89 **Ready to Transform Your AI Agents?**\n\n**Join the revolution in AI tooling!** \ud83d\ude80\n\n```bash\n# Start your MCP server journey\npip install facet-mcp-server\nfacet-mcp start\n```\n\n**From \"creative but unreliable\" to \"high-performance managers\"** \ud83c\udf1f\n\n</div>\n\n---\n\n## \ud83d\udcc4 **License**\n\nThis project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.\n\n---\n\n## \ud83d\udc64 **Author**\n\n**Emil Rokossovskiy** \u2014 [@rokoss21](https://github.com/rokoss21)\n\ud83d\udce7 ecsiar@gmail.com\n\u00a9 2025 Emil Rokossovskiy\n\n---\n\n<div align=\"center\">\n\n## \ud83d\udd17 **Links**\n\n- **[Main FACET Project](https://github.com/rokoss21/FACET)** - Core FACET language and tools\n- **[FACET Documentation](https://github.com/rokoss21/FACET/blob/main/README.md)** - Complete FACET language specification\n- **[PyPI Package](https://pypi.org/project/facet-mcp-server/)** - Install via pip\n\n</div>\n",
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