Expert Registry MCP Server

A high-performance MCP server for expert discovery, registration, and context injection built with FastMCP v2, featuring vector and graph database integration for enhanced semantic search and relationship modeling.

- 🚀High Performance: Multi-layer caching with vector indices for sub-millisecond queries
- 📁File-Based Updates: Hot reload on registry/context file changes
- 🔍Semantic Search: Vector database integration for meaning-based expert discovery
- 🔗Relationship Modeling: Graph database for expert networks and team formation
- 💉Context Injection: AI-powered prompt enhancement with expert knowledge
- 📊Analytics: Performance tracking with collaborative filtering
- 🧠Hybrid Discovery: Combined vector similarity and graph connectivity scoring
- 🐍Python-First: Built with FastMCP v2 for clean, Pythonic code

The easiest way to run the Expert Registry MCP server is using Docker:

# Build and deploy locally ./scripts/build.sh ./scripts/deploy.sh # Or use pre-built image from GitHub Container Registry docker pull ghcr.io/agentience/expert-registry-mcp:latest

- 🐳 Single container service for multiple MCP clients
- 📦 Expert contexts and registry mapped to host for easy editing
- 🔄 Hot reload support when files change on host
- 🌐 SSE transport for client connections
- 🗄️ Includes Neo4j database setup
- 🚀 Production-ready with health checks

SeeDOCKER.mdfor complete deployment guide.

# Create virtual environment and install uv venv uv pip install -e . # Or install directly uv pip install expert-registry-mcp

# ChromaDB is embedded, no separate installation needed # It will create a vector-db directory automatically

# Option 1: Docker (recommended) docker run -d --name neo4j \ -p 7474:7474 -p 7687:7687 \ -e NEO4J_AUTH=neo4j/password \ neo4j:latest # Option 2: Local installation # Download from https://neo4j.com/download/

expert-system/ ├── registry/ │ └── expert-registry.json ├── expert-contexts/ │ ├── aws-amplify-gen2.md │ ├── aws-cloudscape.md │ └── ... └── performance/ └── metrics.json

export EXPERT_SYSTEM_PATH=/path/to/expert-system export NEO4J_URI=bolt://localhost:7687 export NEO4J_PASSWORD=password

# Using FastMCP CLI fastmcp run expert-registry-mcp # Or using Python python -m expert_registry_mcp.server

{ "mcpServers": { "expert-registry": { "command": "uv", "args": ["run", "expert-registry-mcp"], "env": { "EXPERT_SYSTEM_PATH": "/path/to/expert-system", "NEO4J_URI": "bolt://localhost:7687", "NEO4J_PASSWORD": "password" } } } }

# Detect technologies in your project technologies = await expert_detect_technologies( scan_paths=["./src", "./package.json"] ) # Select the best expert with hybrid search result = await expert_smart_discover( context={ "description": "Refactor authentication system using AWS Amplify", "technologies": technologies.technologies, "constraints": ["maintain backward compatibility"], "preferred_strategy": "single" } )

# Load expert context context = await expert_load_context( expert_id=result.expert.id ) # Inject into prompt enhanced_prompt = await expert_inject_context( prompt="Refactor the authentication system", expert_id=result.expert.id, injection_points=["constraints", "patterns", "quality-criteria"] )

# Track usage await expert_track_usage( expert_id=result.expert.id, task_id="auth-refactor-001", outcome={ "success": True, "adherence_score": 9.5, "task_type": "refactoring" } ) # Get analytics analytics = await expert_get_analytics( expert_id=result.expert.id )

- expert_registry_list- List experts with filtering
- expert_registry_get- Get expert details
- expert_registry_search- Search experts by query

- expert_detect_technologies- Detect project technologies
- expert_select_optimal- Select best expert for task
- expert_assess_capability- Assess expert capability
- expert_smart_discover- AI-powered hybrid search (vector + graph)

- expert_semantic_search- Search using natural language
- expert_find_similar- Find similar experts

- expert_explore_network- Explore expert relationships
- expert_find_combinations- Find complementary expert teams

- expert_load_context- Load expert knowledge
- expert_inject_context- Enhance prompts with expertise

- expert_track_usage- Record expert performance
- expert_get_analytics- Get performance metrics

{ "version": "1.0.0", "last_updated": "2025-06-30T00:00:00Z", "experts": [ { "id": "aws-amplify-gen2", "name": "AWS Amplify Gen 2 Expert", "version": "1.0.0", "description": "Expert in AWS Amplify Gen 2 development", "domains": ["backend", "cloud", "serverless"], "specializations": [ { "technology": "AWS Amplify Gen 2", "frameworks": ["AWS CDK", "TypeScript"], "expertise_level": "expert" } ], "workflow_compatibility": { "feature": 0.95, "bug-fix": 0.85, "refactoring": 0.80, "investigation": 0.70, "article": 0.60 }, "constraints": [ "Use TypeScript-first approach", "Follow AWS Well-Architected Framework" ], "patterns": [ "Infrastructure as Code", "Serverless-first architecture" ], "quality_standards": [ "100% type safety", "Comprehensive error handling" ] } ] }

Expert context files are markdown documents inexpert-contexts/:

# AWS Amplify Gen 2 Expert Context ## Constraints - Use TypeScript for all backend code - Follow AWS Well-Architected Framework principles - Implement proper error handling and logging ## Patterns - Infrastructure as Code using CDK - Serverless-first architecture - Event-driven communication ## Quality Standards - 100% TypeScript type coverage - Comprehensive error handling - Unit test coverage > 80%

# Clone repository git clone https://github.com/agentience/expert-registry-mcp cd expert-registry-mcp # Create virtual environment with uv uv venv source .venv/bin/activate # or .venv\Scripts\activate on Windows # Install in development mode uv pip install -e ".[dev]"

# Run all tests pytest # Run with coverage pytest --cov=expert_registry_mcp # Run specific test file pytest tests/test_registry.py

# Format code black src tests # Lint code ruff check src tests # Type checking mypy src

- Registry Cache: 24-hour TTL for expert definitions
- Vector Cache: Embeddings cached until expert updates
- Graph Cache: Relationship queries cached for 10 minutes
- Selection Cache: 5-minute TTL for technology detection
- Context Cache: LRU cache for expert contexts (50 entries)

- ChromaDB: Embedded vector database for semantic search

- Multiple collections for different embedding types
- Automatic embedding generation with sentence-transformers

- Expert-Technology-Task relationships
- Team synergy calculations
- Evolution tracking

- Vector Indices: Annoy indices for ultra-fast similarity search
- Precomputed Combinations: Common expert pairs cached
- Batch Operations: Efficient bulk processing
- Smart Invalidation: Targeted cache updates

- Useswatchdogfor cross-platform file monitoring
- Automatic registry reload and database sync
- No server restart required for updates

- Verify expert ID in registry
- Check file paths are correct
- Ensure registry JSON is valid

- Check expert-contexts directory
- Verify filename matches expert ID
- Ensure .md extension

- File watcher may need restart
- Check file permissions
- Verify EXPERT_SYSTEM_PATH

export FASTMCP_DEBUG=1 expert-registry-mcp

The system uses ChromaDB to enable natural language queries:

# Find experts by meaning, not just keywords results = await expert_semantic_search( query="implement secure authentication with cloud integration", search_mode="hybrid" )

Neo4j powers sophisticated relationship queries:

# Explore expert networks network = await expert_explore_network( start_expert_id="aws-amplify-gen2", depth=2, relationship_types=["SPECIALIZES_IN", "COMPATIBLE_WITH"] )

# Find complementary expert teams teams = await expert_find_combinations( requirements=["AWS Amplify", "React", "DynamoDB"], team_size=3 )

- Fork the repository
- Create your feature branch (git checkout -b feature/amazing-feature)
- Run tests and linting
- Commit your changes (git commit -m 'Add amazing feature')
- Push to the branch (git push origin feature/amazing-feature)
- Open a Pull Request

MIT License - see LICENSE file for details

- Documentation:https://github.com/agentience/expert-registry-mcp
- Issues:https://github.com/agentience/expert-registry-mcp/issues
- Discussions:https://github.com/agentience/expert-registry-mcp/discussions

SerpApi MCP Server for Google and other search engine results