# 🏗️ Architecture-Focused MCP GLiNER Server
> **Enhanced GLiNER-based entity extraction for software architecture documents with TOGAF ADM phase awareness and role-based contextual processing, implemented as an MCP (Model Context Protocol) server.**
[](https://www.python.org/downloads/)
[](https://github.com/jlowin/fastmcp)
[](https://github.com/urchade/GLiNER)
[](https://github.com/modelcontextprotocol)
## 🎯 Overview
This project transforms the basic GLiNER NER model into a specialized **MCP (Model Context Protocol) server** for software architecture document analysis. It provides intelligent entity extraction that understands TOGAF ADM phases, architecture roles, and contextual relationships to help architects make better decisions and retrieve relevant guidelines.
The server implements the MCP protocol, making it compatible with Claude Desktop, Cody, and other MCP-enabled clients.
### Key Differentiators
- **Architecture-Specific Intelligence**: Pre-configured with 200+ architecture-specific entity labels
- **TOGAF ADM Integration**: Phase-aware processing for all 10 TOGAF ADM phases
- **Role-Based Filtering**: Customized extraction for different architect roles
- **Contextual Scoring**: Relevance-weighted entity scoring based on project context
- **Document Classification**: Automatic analysis of document type and complexity
## 🚀 Quick Start
### 🎯 Easiest Way (Using uvx)
```bash
# Install uvx if you don't have it
curl -LsSf https://astral.sh/uv/install.sh | sh
# Run the MCP server directly (no installation needed!)
uvx mcp-architecture-gliner
```
Then configure your MCP client (like Claude Desktop) to use:
```json
{
"mcpServers": {
"architecture-gliner": {
"command": "uvx",
"args": ["mcp-architecture-gliner"]
}
}
}
```
### 🔧 Development Setup
### Prerequisites
- Python 3.9+
- macOS (optimized for Apple Silicon M1/M2/M3)
- 8GB+ RAM (16GB+ recommended for large models)
### Installation
```bash
# Clone the repository
git clone <repository-url>
cd mcp-gliner
# Install uv (if not already installed)
brew install uv
# Create and activate environment
uv venv
source .venv/bin/activate
# Install dependencies
uv pip install -r requirements.txt
```
### Start the MCP Server
#### Option 1: Using uvx (Recommended - after publishing)
```bash
# Run directly with uvx (no installation needed)
uvx mcp-architecture-gliner
# The server will run in STDIO mode for MCP communication
```
#### Option 2: Local Development
```bash
# Clone and set up locally
git clone <repository-url>
cd mcp-gliner
# Install and run
uv venv
source .venv/bin/activate
uv pip install -r requirements.txt
python mcp_server.py
```
## 🧪 Usage Examples
### 1. MCP Client Integration
#### Claude Desktop Configuration
**Option 1: Using uvx (Recommended)**
Add to your Claude Desktop MCP settings:
```json
{
"mcpServers": {
"architecture-gliner": {
"command": "uvx",
"args": ["mcp-architecture-gliner"]
}
}
}
```
**Option 2: Local Development**
```json
{
"mcpServers": {
"architecture-gliner": {
"command": "python",
"args": ["/absolute/path/to/mcp-gliner/mcp_server.py"],
"env": {
"HF_HUB_ENABLE_HF_TRANSFER": "0"
}
}
}
}
```
#### Available MCP Tools
The server provides 6 MCP tools:
1. **`extract_architecture_entities`** - Extract entities with phase/role context
2. **`analyze_architecture_document`** - Analyze document type and complexity
3. **`get_architecture_labels`** - Get available labels by category
4. **`get_phase_specific_labels`** - Get TOGAF phase-specific labels
5. **`get_role_specific_labels`** - Get role-specific labels
6. **`get_gliner_model_info`** - Get model capabilities
### 2. Direct Usage (for testing)
```python
import asyncio
from tools.architecture_gliner.models import ArchitectureExtractor
async def extract_example():
extractor = ArchitectureExtractor(model_size='medium-v2.1')
text = """
The solution architect designed a microservices architecture
using Docker containers and Kubernetes orchestration to meet
scalability and availability requirements.
"""
entities = extractor.extract_entities(text)
for entity in entities:
print(f"• {entity['text']} → {entity['label']} (score: {entity['score']:.3f})")
asyncio.run(extract_example())
```
### 2. Phase-Aware Processing
```python
# Extract entities relevant to Architecture Vision phase
entities = extractor.extract_entities(
text=architecture_document,
phase="architecture_vision", # TOGAF ADM Phase A
include_context=True
)
# Filter for high-relevance entities
relevant_entities = [
e for e in entities
if e.get('phase_relevant', False) and e.get('contextual_score', 0) > 0.7
]
```
### 3. Role-Based Filtering
```python
# Extract entities from a Solution Architect's perspective
solution_entities = extractor.extract_entities(
text=technical_spec,
role="solution_architect",
categories=["patterns", "quality_attributes", "technical_context"]
)
```
### 4. Document Analysis
```python
# Automatically analyze document characteristics
analysis = extractor.analyze_document_type(document_text)
print(f"Document complexity: {analysis['document_complexity']}")
print(f"Suggested model: {analysis['suggested_model']}")
print(f"Likely phases: {analysis['likely_phases'][:3]}")
```
### 5. API Usage
```bash
# Extract entities via REST API
curl -X POST "http://localhost:8000/extract-entities" \
-H "Content-Type: application/json" \
-d '{
"text": "The enterprise architect defined microservices patterns for scalability.",
"phase": "architecture_vision",
"role": "enterprise_architect",
"threshold": 0.5
}'
# Get phase-specific labels
curl "http://localhost:8000/labels/phase/business_architecture"
# Analyze document
curl -X POST "http://localhost:8000/analyze-document" \
-H "Content-Type: application/json" \
-d '{
"text": "Technical specification document content...",
"threshold": 0.3
}'
```
## 📚 Architecture Categories
The system recognizes entities across multiple architecture domains:
### TOGAF ADM Phases
- Preliminary Phase
- Architecture Vision (Phase A)
- Business Architecture (Phase B)
- Information Systems Architecture (Phase C)
- Technology Architecture (Phase D)
- Opportunities & Solutions (Phase E)
- Migration Planning (Phase F)
- Implementation Governance (Phase G)
- Architecture Change Management (Phase H)
- Requirements Management
### Architecture Roles
- Enterprise Architect
- Solution Architect
- Business Architect
- Data Architect
- Application Architect
- Technology Architect
- Security Architect
- Infrastructure Architect
### Architecture Patterns
- Layered Architecture
- Microservices Architecture
- Service-Oriented Architecture
- Event-Driven Architecture
- Serverless Architecture
- Hexagonal Architecture
- Clean Architecture
### Quality Attributes
- Performance
- Scalability
- Availability
- Reliability
- Security
- Maintainability
- Usability
- Portability
## 🔧 Configuration
### Model Selection
```python
# Choose model size based on your needs
extractors = {
'base': ArchitectureExtractor(model_size='base'), # ~100M params, fastest
'medium': ArchitectureExtractor(model_size='medium-v2.1'), # ~300M params, balanced
'large': ArchitectureExtractor(model_size='large-v2.1'), # ~1B params, most accurate
}
```
### Custom Labels
Edit `tools/architecture_gliner/config/architecture_labels.yaml` to add organization-specific terms:
```yaml
architecture_labels:
custom_patterns:
- "my-company-pattern"
- "legacy-integration-pattern"
custom_technologies:
- "proprietary-platform"
- "internal-framework"
```
## 🔍 API Reference
### Core Endpoints
| Endpoint | Method | Description |
|----------|--------|-------------|
| `/extract-entities` | POST | Extract architecture entities with context |
| `/analyze-document` | POST | Analyze document type and characteristics |
| `/labels` | GET | Get available architecture labels |
| `/labels/phase/{phase}` | GET | Get phase-specific labels |
| `/labels/role/{role}` | GET | Get role-specific labels |
| `/model-info` | GET | Get model capabilities and information |
| `/health` | GET | Health check endpoint |
### Request/Response Models
#### Entity Extraction Request
```json
{
"text": "string",
"labels": ["string"] | null,
"phase": "string" | null,
"role": "string" | null,
"categories": ["string"] | null,
"threshold": 0.5,
"model_size": "medium-v2.1",
"include_context": true
}
```
#### Entity Response
```json
{
"entities": [
{
"text": "microservices architecture",
"label": "microservices architecture",
"start": 45,
"end": 70,
"score": 0.92,
"categories": ["patterns"],
"phase_relevant": true,
"contextual_score": 0.98
}
],
"total_count": 1,
"processing_info": {
"model_size": "medium-v2.1",
"phase_context": "architecture_vision",
"role_context": "solution_architect"
}
}
```
## 🧪 Examples
Run the comprehensive examples:
```bash
# Direct Python usage examples
python examples/architecture_example.py
# API client examples
python examples/api_client_example.py
```
## 🏗️ Architecture
```
mcp-gliner/
├── tools/architecture_gliner/ # Core implementation
│ ├── models/ # GLiNER integration
│ │ └── architecture_extractor.py # Main extraction logic
│ ├── config/ # Configuration files
│ │ └── architecture_labels.yaml # Architecture-specific labels
│ └── tool.py # FastMCP tool integration
├── examples/ # Usage examples
├── server.py # FastAPI server
└── requirements.txt # Dependencies
```
## 🔬 Research Applications
### 1. Contextual Architecture Guidance
Extract entities to provide relevant architecture guidelines based on:
- Current project phase (TOGAF ADM)
- Architect role and responsibilities
- Document type and complexity
### 2. Architecture Knowledge Graph
Build relationships between:
- Architecture patterns and quality attributes
- Business requirements and technical solutions
- Stakeholders and architectural decisions
### 3. Document Quality Assurance
Automatically validate:
- Completeness of architectural artifacts
- Compliance with architecture principles
- Consistency across document sets
### 4. Architecture Decision Support
Enable intelligent retrieval of:
- Relevant design patterns
- Best practices and guidelines
- Historical decisions and rationale
## 🚧 Future Roadmap
- [ ] **Architecture Guidelines Database**: Integrate comprehensive knowledge base
- [ ] **Relationship Extraction**: Identify connections between architectural concepts
- [ ] **Multi-document Analysis**: Cross-reference entities across document sets
- [ ] **Template Generation**: Create structured outputs for deliverables
- [ ] **RAG Integration**: Context-aware architecture guidance retrieval
- [ ] **Compliance Checking**: Automated validation against standards
- [ ] **Export Capabilities**: Generate reports in multiple formats
## 🤝 Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- [GLiNER](https://github.com/urchade/GLiNER) - Zero-shot Named Entity Recognition
- [FastAPI](https://fastapi.tiangolo.com/) - Modern Python web framework
- [TOGAF](https://www.opengroup.org/togaf) - Enterprise Architecture methodology
- [FastMCP](https://github.com/jlowin/fastmcp) - MCP server framework
---
**Built for architects, by architects** 🏗️
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"description": "# \ud83c\udfd7\ufe0f Architecture-Focused MCP GLiNER Server\n\n> **Enhanced GLiNER-based entity extraction for software architecture documents with TOGAF ADM phase awareness and role-based contextual processing, implemented as an MCP (Model Context Protocol) server.**\n\n[](https://www.python.org/downloads/)\n[](https://github.com/jlowin/fastmcp)\n[](https://github.com/urchade/GLiNER)\n[](https://github.com/modelcontextprotocol)\n\n## \ud83c\udfaf Overview\n\nThis project transforms the basic GLiNER NER model into a specialized **MCP (Model Context Protocol) server** for software architecture document analysis. It provides intelligent entity extraction that understands TOGAF ADM phases, architecture roles, and contextual relationships to help architects make better decisions and retrieve relevant guidelines.\n\nThe server implements the MCP protocol, making it compatible with Claude Desktop, Cody, and other MCP-enabled clients.\n\n### Key Differentiators\n\n- **Architecture-Specific Intelligence**: Pre-configured with 200+ architecture-specific entity labels\n- **TOGAF ADM Integration**: Phase-aware processing for all 10 TOGAF ADM phases\n- **Role-Based Filtering**: Customized extraction for different architect roles\n- **Contextual Scoring**: Relevance-weighted entity scoring based on project context\n- **Document Classification**: Automatic analysis of document type and complexity\n\n## \ud83d\ude80 Quick Start\n\n### \ud83c\udfaf Easiest Way (Using uvx)\n\n```bash\n# Install uvx if you don't have it\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n\n# Run the MCP server directly (no installation needed!)\nuvx mcp-architecture-gliner\n```\n\nThen configure your MCP client (like Claude Desktop) to use:\n```json\n{\n \"mcpServers\": {\n \"architecture-gliner\": {\n \"command\": \"uvx\",\n \"args\": [\"mcp-architecture-gliner\"]\n }\n }\n}\n```\n\n### \ud83d\udd27 Development Setup\n\n### Prerequisites\n\n- Python 3.9+\n- macOS (optimized for Apple Silicon M1/M2/M3)\n- 8GB+ RAM (16GB+ recommended for large models)\n\n### Installation\n\n```bash\n# Clone the repository\ngit clone <repository-url>\ncd mcp-gliner\n\n# Install uv (if not already installed)\nbrew install uv\n\n# Create and activate environment\nuv venv\nsource .venv/bin/activate\n\n# Install dependencies\nuv pip install -r requirements.txt\n```\n\n### Start the MCP Server\n\n#### Option 1: Using uvx (Recommended - after publishing)\n```bash\n# Run directly with uvx (no installation needed)\nuvx mcp-architecture-gliner\n\n# The server will run in STDIO mode for MCP communication\n```\n\n#### Option 2: Local Development\n```bash\n# Clone and set up locally\ngit clone <repository-url>\ncd mcp-gliner\n\n# Install and run\nuv venv\nsource .venv/bin/activate\nuv pip install -r requirements.txt\npython mcp_server.py\n```\n\n## \ud83e\uddea Usage Examples\n\n### 1. MCP Client Integration\n\n#### Claude Desktop Configuration\n\n**Option 1: Using uvx (Recommended)**\nAdd to your Claude Desktop MCP settings:\n```json\n{\n \"mcpServers\": {\n \"architecture-gliner\": {\n \"command\": \"uvx\",\n \"args\": [\"mcp-architecture-gliner\"]\n }\n }\n}\n```\n\n**Option 2: Local Development**\n```json\n{\n \"mcpServers\": {\n \"architecture-gliner\": {\n \"command\": \"python\",\n \"args\": [\"/absolute/path/to/mcp-gliner/mcp_server.py\"],\n \"env\": {\n \"HF_HUB_ENABLE_HF_TRANSFER\": \"0\"\n }\n }\n }\n}\n```\n\n#### Available MCP Tools\n\nThe server provides 6 MCP tools:\n\n1. **`extract_architecture_entities`** - Extract entities with phase/role context\n2. **`analyze_architecture_document`** - Analyze document type and complexity \n3. **`get_architecture_labels`** - Get available labels by category\n4. **`get_phase_specific_labels`** - Get TOGAF phase-specific labels\n5. **`get_role_specific_labels`** - Get role-specific labels\n6. **`get_gliner_model_info`** - Get model capabilities\n\n### 2. Direct Usage (for testing)\n\n```python\nimport asyncio\nfrom tools.architecture_gliner.models import ArchitectureExtractor\n\nasync def extract_example():\n extractor = ArchitectureExtractor(model_size='medium-v2.1')\n \n text = \"\"\"\n The solution architect designed a microservices architecture \n using Docker containers and Kubernetes orchestration to meet \n scalability and availability requirements.\n \"\"\"\n \n entities = extractor.extract_entities(text)\n \n for entity in entities:\n print(f\"\u2022 {entity['text']} \u2192 {entity['label']} (score: {entity['score']:.3f})\")\n\nasyncio.run(extract_example())\n```\n\n### 2. Phase-Aware Processing\n\n```python\n# Extract entities relevant to Architecture Vision phase\nentities = extractor.extract_entities(\n text=architecture_document,\n phase=\"architecture_vision\", # TOGAF ADM Phase A\n include_context=True\n)\n\n# Filter for high-relevance entities\nrelevant_entities = [\n e for e in entities \n if e.get('phase_relevant', False) and e.get('contextual_score', 0) > 0.7\n]\n```\n\n### 3. Role-Based Filtering\n\n```python\n# Extract entities from a Solution Architect's perspective\nsolution_entities = extractor.extract_entities(\n text=technical_spec,\n role=\"solution_architect\",\n categories=[\"patterns\", \"quality_attributes\", \"technical_context\"]\n)\n```\n\n### 4. Document Analysis\n\n```python\n# Automatically analyze document characteristics\nanalysis = extractor.analyze_document_type(document_text)\n\nprint(f\"Document complexity: {analysis['document_complexity']}\")\nprint(f\"Suggested model: {analysis['suggested_model']}\")\nprint(f\"Likely phases: {analysis['likely_phases'][:3]}\")\n```\n\n### 5. API Usage\n\n```bash\n# Extract entities via REST API\ncurl -X POST \"http://localhost:8000/extract-entities\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"text\": \"The enterprise architect defined microservices patterns for scalability.\",\n \"phase\": \"architecture_vision\",\n \"role\": \"enterprise_architect\",\n \"threshold\": 0.5\n }'\n\n# Get phase-specific labels\ncurl \"http://localhost:8000/labels/phase/business_architecture\"\n\n# Analyze document\ncurl -X POST \"http://localhost:8000/analyze-document\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"text\": \"Technical specification document content...\",\n \"threshold\": 0.3\n }'\n```\n\n## \ud83d\udcda Architecture Categories\n\nThe system recognizes entities across multiple architecture domains:\n\n### TOGAF ADM Phases\n- Preliminary Phase\n- Architecture Vision (Phase A)\n- Business Architecture (Phase B)\n- Information Systems Architecture (Phase C)\n- Technology Architecture (Phase D)\n- Opportunities & Solutions (Phase E)\n- Migration Planning (Phase F)\n- Implementation Governance (Phase G)\n- Architecture Change Management (Phase H)\n- Requirements Management\n\n### Architecture Roles\n- Enterprise Architect\n- Solution Architect\n- Business Architect\n- Data Architect\n- Application Architect\n- Technology Architect\n- Security Architect\n- Infrastructure Architect\n\n### Architecture Patterns\n- Layered Architecture\n- Microservices Architecture\n- Service-Oriented Architecture\n- Event-Driven Architecture\n- Serverless Architecture\n- Hexagonal Architecture\n- Clean Architecture\n\n### Quality Attributes\n- Performance\n- Scalability\n- Availability\n- Reliability\n- Security\n- Maintainability\n- Usability\n- Portability\n\n## \ud83d\udd27 Configuration\n\n### Model Selection\n\n```python\n# Choose model size based on your needs\nextractors = {\n 'base': ArchitectureExtractor(model_size='base'), # ~100M params, fastest\n 'medium': ArchitectureExtractor(model_size='medium-v2.1'), # ~300M params, balanced\n 'large': ArchitectureExtractor(model_size='large-v2.1'), # ~1B params, most accurate\n}\n```\n\n### Custom Labels\n\nEdit `tools/architecture_gliner/config/architecture_labels.yaml` to add organization-specific terms:\n\n```yaml\narchitecture_labels:\n custom_patterns:\n - \"my-company-pattern\"\n - \"legacy-integration-pattern\"\n \n custom_technologies:\n - \"proprietary-platform\"\n - \"internal-framework\"\n```\n\n## \ud83d\udd0d API Reference\n\n### Core Endpoints\n\n| Endpoint | Method | Description |\n|----------|--------|-------------|\n| `/extract-entities` | POST | Extract architecture entities with context |\n| `/analyze-document` | POST | Analyze document type and characteristics |\n| `/labels` | GET | Get available architecture labels |\n| `/labels/phase/{phase}` | GET | Get phase-specific labels |\n| `/labels/role/{role}` | GET | Get role-specific labels |\n| `/model-info` | GET | Get model capabilities and information |\n| `/health` | GET | Health check endpoint |\n\n### Request/Response Models\n\n#### Entity Extraction Request\n```json\n{\n \"text\": \"string\",\n \"labels\": [\"string\"] | null,\n \"phase\": \"string\" | null,\n \"role\": \"string\" | null,\n \"categories\": [\"string\"] | null,\n \"threshold\": 0.5,\n \"model_size\": \"medium-v2.1\",\n \"include_context\": true\n}\n```\n\n#### Entity Response\n```json\n{\n \"entities\": [\n {\n \"text\": \"microservices architecture\",\n \"label\": \"microservices architecture\",\n \"start\": 45,\n \"end\": 70,\n \"score\": 0.92,\n \"categories\": [\"patterns\"],\n \"phase_relevant\": true,\n \"contextual_score\": 0.98\n }\n ],\n \"total_count\": 1,\n \"processing_info\": {\n \"model_size\": \"medium-v2.1\",\n \"phase_context\": \"architecture_vision\",\n \"role_context\": \"solution_architect\"\n }\n}\n```\n\n## \ud83e\uddea Examples\n\nRun the comprehensive examples:\n\n```bash\n# Direct Python usage examples\npython examples/architecture_example.py\n\n# API client examples\npython examples/api_client_example.py\n```\n\n## \ud83c\udfd7\ufe0f Architecture\n\n```\nmcp-gliner/\n\u251c\u2500\u2500 tools/architecture_gliner/ # Core implementation\n\u2502 \u251c\u2500\u2500 models/ # GLiNER integration\n\u2502 \u2502 \u2514\u2500\u2500 architecture_extractor.py # Main extraction logic\n\u2502 \u251c\u2500\u2500 config/ # Configuration files\n\u2502 \u2502 \u2514\u2500\u2500 architecture_labels.yaml # Architecture-specific labels\n\u2502 \u2514\u2500\u2500 tool.py # FastMCP tool integration\n\u251c\u2500\u2500 examples/ # Usage examples\n\u251c\u2500\u2500 server.py # FastAPI server\n\u2514\u2500\u2500 requirements.txt # Dependencies\n```\n\n## \ud83d\udd2c Research Applications\n\n### 1. Contextual Architecture Guidance\nExtract entities to provide relevant architecture guidelines based on:\n- Current project phase (TOGAF ADM)\n- Architect role and responsibilities\n- Document type and complexity\n\n### 2. Architecture Knowledge Graph\nBuild relationships between:\n- Architecture patterns and quality attributes\n- Business requirements and technical solutions\n- Stakeholders and architectural decisions\n\n### 3. Document Quality Assurance\nAutomatically validate:\n- Completeness of architectural artifacts\n- Compliance with architecture principles\n- Consistency across document sets\n\n### 4. Architecture Decision Support\nEnable intelligent retrieval of:\n- Relevant design patterns\n- Best practices and guidelines\n- Historical decisions and rationale\n\n## \ud83d\udea7 Future Roadmap\n\n- [ ] **Architecture Guidelines Database**: Integrate comprehensive knowledge base\n- [ ] **Relationship Extraction**: Identify connections between architectural concepts\n- [ ] **Multi-document Analysis**: Cross-reference entities across document sets\n- [ ] **Template Generation**: Create structured outputs for deliverables\n- [ ] **RAG Integration**: Context-aware architecture guidance retrieval\n- [ ] **Compliance Checking**: Automated validation against standards\n- [ ] **Export Capabilities**: Generate reports in multiple formats\n\n## \ud83e\udd1d Contributing\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\ude4f Acknowledgments\n\n- [GLiNER](https://github.com/urchade/GLiNER) - Zero-shot Named Entity Recognition\n- [FastAPI](https://fastapi.tiangolo.com/) - Modern Python web framework\n- [TOGAF](https://www.opengroup.org/togaf) - Enterprise Architecture methodology\n- [FastMCP](https://github.com/jlowin/fastmcp) - MCP server framework\n\n---\n\n**Built for architects, by architects** \ud83c\udfd7\ufe0f",
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