graphragmcp

lyndonkl/graphragmcp

3.2

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GraphRAG MCP Server provides structured access to knowledge about Knowledge Graph Construction & Retrieval Strategies for LLM Reasoning.

GraphRAG MCP Server

A comprehensive Model Context Protocol (MCP) server providing structured access to knowledge about Knowledge Graph Construction & Retrieval Strategies for LLM Reasoning. This server enables AI agents to access detailed research findings, implementation patterns, and best practices for building graph-enhanced retrieval-augmented generation systems.

🎯 What This Server Provides

This MCP server offers hierarchical access to 59 pages of comprehensive research covering:

  • 7 Knowledge Graph Construction Patterns for LLM reasoning
  • 5 Embedding Fusion Strategies combining semantic and structural representations
  • 6 Retrieval & Search Strategies for graph+vector hybrid systems
  • Architectural Trade-offs analysis (LPG, RDF/OWL, hypergraphs, factor graphs)
  • Recent Research & Industry Landscape (2022-present)
  • Technology Stacks & Frameworks survey
  • Consolidated Pattern Catalog with implementation guidance

The server provides multiple access methods:

  • 25 Knowledge Resources - Hierarchical content from overview to specific techniques
  • 4 Specialized Prompts - Domain-specific analysis and guidance
  • 10 MCP Tools - Direct access for Claude Code agents and programmatic use

Content is organized into 3 hierarchical levels:

  1. Overview - High-level summaries and abstracts
  2. Main Knowledge Areas - Detailed coverage of each topic
  3. Specific Techniques - Implementation details for individual patterns

📁 Project Structure

graphragmcp/
├── README.md                    # This file
├── requirements.txt            # Python dependencies
├── environment.yml            # Conda environment specification
├── setup.py                   # Python package setup
├── .gitignore                # Git ignore rules
├── src/
│   ├── main.py              # Main entry point (new modular architecture)
│   ├── server.py            # Legacy monolithic server (for compatibility)
│   └── graphrag_mcp/        # Modular architecture package
│       ├── __init__.py      # Package exports
│       ├── server.py        # Main server class
│       ├── config.py        # Configuration management
│       ├── resources/       # Resource management
│       │   ├── __init__.py
│       │   ├── definitions.py  # Resource definitions
│       │   └── registry.py     # Resource registry
│       ├── prompts/         # Prompt management
│       │   ├── __init__.py
│       │   ├── definitions.py  # Prompt definitions
│       │   ├── registry.py     # Prompt registry
│       │   └── generators.py   # Prompt generators
│       ├── content/         # Content generation
│       │   ├── __init__.py
│       │   └── loader.py       # Content loader from original
│       └── utils/           # Utilities and exceptions
│           ├── __init__.py
│           └── exceptions.py   # Custom exceptions
├── agents/
│   └── graphrag_specialist.md  # Specialized GraphRAG agent for Claude Code
├── docs/
│   └── Knowledge Graph Construction & Retrieval Strategies for LLM Reasoning.pdf
├── examples/
│   ├── test_client.py        # Test client to verify server functionality
│   ├── claude_desktop_config.json  # Configuration for Claude Desktop
│   └── .claude/agents/      # Example Claude Code project structure
│       └── graphrag_specialist.md  # Agent configuration example
└── tests/                    # Future test files

🚀 Quick Start

Option 1: Using uv (Recommended - Modern Python)

  1. Install uv (if not already installed):

    # macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Or with pip
pip install uv

  2. Install and run the server:

    cd graphragmcp

# Install dependencies and run (uv handles everything)
uv run src/main.py

# Or install as a package and run
uv pip install -e .
uv run graphrag-mcp

Option 2: Using Conda

  1. Install Conda (if not already installed):

  2. Create and activate the environment:

    cd graphragmcp
conda env create -f environment.yml
conda activate graphragmcp

  3. Run the server:

    # New modular architecture (recommended)
python src/main.py

# Or legacy server (for compatibility)
python src/server.py

Option 3: Using pip + venv

  1. Create a virtual environment:

    cd graphragmcp
python -m venv venv

# Activate on macOS/Linux:
source venv/bin/activate

# Activate on Windows:
venv\\Scripts\\activate

  2. Install dependencies:

    pip install -r requirements.txt
# Or install as editable package
pip install -e .

  3. Run the server:

    # New modular architecture (recommended)
python src/main.py

# Or as installed package
graphrag-mcp

# Or legacy server (for compatibility)
python src/server.py

🔧 Configuration

For Claude Desktop

  1. Locate your Claude Desktop config file:

    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\\Claude\\claude_desktop_config.json

  2. Choose your configuration method:

    Option A: Using uv (Recommended - Best compatibility)
    {
  "mcpServers": {
    "graphrag-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/graphragmcp",
        "run",
        "src/main.py"
      ]
    }
  }
}
    Option B: Using uv with installed package
    {
  "mcpServers": {
    "graphrag-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/graphragmcp",
        "run",
        "graphrag-mcp"
      ]
    }
  }
}
    Option C: Using Python directly
    {
  "mcpServers": {
    "graphrag-mcp": {
      "command": "python",
      "args": ["/ABSOLUTE/PATH/TO/graphragmcp/src/main.py"]
    }
  }
}
    Option D: Using Conda environment
    {
  "mcpServers": {
    "graphrag-mcp": {
      "command": "conda",
      "args": [
        "run",
        "-n",
        "graphragmcp",
        "python",
        "/ABSOLUTE/PATH/TO/graphragmcp/src/main.py"
      ]
    }
  }
}

    ⚠️ Important:

    • Replace /ABSOLUTE/PATH/TO/graphragmcp with your actual project path
    • Use absolute paths only - relative paths will not work
    • For Windows, use forward slashes or escaped backslashes in paths

    🔧 Troubleshooting uv path issues: If you get spawn uv ENOENT errors, use the full path to uv:

    # Find where uv is installed
which uv

    Then use the full path in your config:

    {
  "mcpServers": {
    "graphrag-mcp": {
      "command": "/Users/YOUR_USERNAME/.local/bin/uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/graphragmcp",
        "run",
        "src/main.py"
      ]
    }
  }
}

  3. Restart Claude Desktop to load the server.

  4. Verify the server is working:

    • Look for GraphRAG resources in Claude Desktop
    • Check that you can ask GraphRAG-related questions
    • If issues occur, check Claude Desktop's logs

For Claude Code Integration

Once configured in Claude Desktop, you can import the server into Claude Code:

  1. Import from Claude Desktop (macOS/WSL only):

    claude mcp add-from-claude-desktop

    Then select the graphrag-mcp server when prompted.

  2. Verify the import:

    claude mcp list

    You should see graphrag-mcp in the list.

  3. Manual configuration (if automatic import doesn't work): Add to your Claude Code configuration:

    claude mcp add graphrag-mcp --command python --args "/ABSOLUTE/PATH/TO/graphragmcp/src/main.py"

For Other MCP Clients

The server follows the MCP standard and should work with any compatible client. Use:

  • Command: python
  • Args: ["/path/to/graphragmcp/src/main.py"] (recommended) or ["/path/to/graphragmcp/src/server.py"] (legacy)

🤖 GraphRAG Specialist Agent Integration

This project includes a specialized GraphRAG agent designed to work with Claude Code, providing expert-level assistance with Knowledge Graph Construction & Retrieval strategies.

🎯 Agent Capabilities

The GraphRAG Specialist Agent provides:

  • 🗂️ Knowledge Graph Construction: Expert guidance on LLM-assisted entity extraction, event reification, layered architectures, provenance tracking, temporal modeling, and hybrid symbolic-vector integration
  • 🔗 Embedding & Representation Strategies: Advanced techniques for node, edge, path, and subgraph embeddings with multi-modal fusion
  • 🔍 Retrieval & Search Orchestration: Implementation guidance for global-first, local-first, U-shaped hybrid, query decomposition, temporal, and constraint-guided retrieval strategies
  • 🏗️ Architecture & Technology Stacks: Comprehensive knowledge of graph databases, vector databases, frameworks, and cloud platform optimization
  • 🛠️ Implementation Guidance: Step-by-step roadmaps, code examples, performance optimization, and production deployment strategies

📋 Setup Instructions

Option 1: Using Claude Code with MCP Integration (Recommended)

  1. Configure the MCP server in Claude Desktop (see configuration section above)

  2. Import the MCP server into Claude Code:

    # Import from Claude Desktop (macOS/WSL only)
claude mcp add-from-claude-desktop
# Select graphrag-mcp when prompted

# Or add manually
claude mcp add graphrag-mcp --command python --args "/ABSOLUTE/PATH/TO/graphragmcp/src/main.py"

  3. Verify MCP server is available:

    claude mcp list
# Should show graphrag-mcp in the list

  4. Add the GraphRAG Specialist Agent:

    # Create the agents directory in your project
mkdir -p .claude/agents

# Copy the agent configuration
cp agents/graphrag_specialist.md .claude/agents/

    Or manually place graphrag_specialist.md in your project's .claude/agents/ directory.

  5. Start using the integrated system:

    • The agent will be automatically available in Claude Code
    • Reference it with @graphrag-specialist or by asking GraphRAG-related questions
    • The agent will automatically use the MCP server to provide comprehensive, research-backed answers
    • Claude Code will have direct access to all 25 GraphRAG resources and 4 specialized prompts
Option 2: Global Agent Installation

To make the agent available across all Claude Code projects:

  1. Install globally:

    # Create global agents directory
mkdir -p ~/.claude/agents

# Copy the agent configuration
cp agents/graphrag_specialist.md ~/.claude/agents/

  2. The agent will be available in all Claude Code projects

Option 3: Custom Agent Configuration

If you want to customize the agent:

  1. Copy and modify the agent file:

    cp agents/graphrag_specialist.md .claude/agents/my-custom-graphrag-agent.md

  2. Edit the YAML frontmatter:

    • Change the name field to your custom name
    • Modify the description for different specialization
    • Adjust the system prompt as needed

  3. Ensure MCP server accessibility:

    • The agent relies on the GraphRAG MCP server for detailed knowledge
    • Make sure the server is running and accessible

🚀 Using the GraphRAG Agent

Once configured, you can interact with the GraphRAG Specialist Agent for:

🎯 Use Case Analysis
@graphrag-specialist I need to build a knowledge graph for a healthcare application that processes patient records and medical literature. What GraphRAG patterns should I use?
🛠️ Implementation Guidance
@graphrag-specialist How do I implement a U-shaped hybrid retrieval strategy using Neo4j and Pinecone?
📊 Architecture Decisions
@graphrag-specialist Should I use LPG or RDF for a financial compliance knowledge graph with 50M entities?
🔧 Technology Stack Recommendations
@graphrag-specialist What's the best technology stack for a scalable GraphRAG system on AWS?
📈 Performance Optimization
@graphrag-specialist How can I optimize my GraphRAG system's query performance for real-time applications?

🔄 Agent + MCP Server Workflow

The GraphRAG specialist agent uses MCP tools to provide research-backed guidance:

  1. Analyzes your question to understand GraphRAG requirements
  2. Executes relevant MCP tools to access specific knowledge resources:
    • get_construction_patterns for pattern recommendations
    • analyze_graphrag_pattern for use case-specific analysis
    • query_graphrag_resource for detailed technical content
    • compare_architectures for technology decisions
  3. Combines multiple tool results for comprehensive analysis
  4. Provides evidence-based recommendations with source references
  5. Includes implementation guidance and code examples
  6. Suggests evaluation metrics and optimization strategies
  7. Cites specific tools and resources used for transparency

💡 Example Interaction

User: "I need to implement GraphRAG for a legal document analysis system. The system needs to handle complex multi-party contracts and regulatory relationships."

GraphRAG Agent Response:

  1. Uses get_construction_patterns tool to access all available patterns
  2. Executes analyze_graphrag_pattern tool with legal domain context
  3. Calls query_graphrag_resource for detailed Event Reification pattern
  4. Uses compare_architectures tool to recommend Neo4j + Pinecone stack
  5. Provides implementation roadmap with specific phases and reasoning
  6. Includes code examples from pattern-specific resources
  7. Recommends evaluation metrics based on research findings
  8. Cites all tools used: Shows transparency in knowledge sources

🛡️ Best Practices

  • Start with requirements analysis: The agent will help you identify the best patterns
  • Ask for implementation details: Get specific code examples and architectural guidance
  • Request performance considerations: Understand scaling and optimization strategies
  • Validate recommendations: The agent provides research-backed suggestions you can verify

🧪 Testing the Server

Basic Functionality Test

Run the included test client to verify everything works:

cd examples
python test_client.py

This will:

  • Connect to the server
  • List available resources
  • Read sample content from key resources
  • Display available prompts

Manual Testing

You can also test the server manually using MCP tools or by examining the server output when it starts.

📊 Available Resources

The server provides 27 knowledge resources organized hierarchically:

Level 1: Overview

  • graphrag://overview - Comprehensive overview of GraphRAG research

Level 2: Main Knowledge Areas

  • graphrag://construction-patterns - 7 key construction patterns
  • graphrag://embedding-strategies - 5 embedding fusion strategies
  • graphrag://retrieval-strategies - 6 retrieval orchestration strategies
  • graphrag://architectural-tradeoffs - Graph model architecture analysis
  • graphrag://literature-landscape - Recent research & industry practice
  • graphrag://technology-stacks - Frameworks & technology survey
  • graphrag://pattern-catalog - Consolidated design patterns

Level 3: Detailed Techniques

  • Construction Patterns: LLM-assisted extraction, event reification, layered graphs, provenance layering, temporal modeling, hybrid symbolic-vector
  • Embedding Strategies: Node embeddings, edge embeddings, path embeddings, subgraph embeddings, joint representation fusion
  • Retrieval Strategies: Global-first, local-first, U-shaped hybrid, query decomposition, temporal retrieval, constraint-guided filtering

🤖 Available Prompts

The server includes 4 specialized prompts for GraphRAG analysis:

  1. analyze-graphrag-pattern - Analyze best patterns for specific use cases
  2. design-knowledge-graph - Get design guidance for knowledge graphs
  3. implement-retrieval-strategy - Implementation guidance for retrieval strategies
  4. compare-architectures - Compare different graph architectures

🔧 Available MCP Tools

The server provides 10 MCP tools that enable Claude Code agents to access the knowledge base:

🎯 Generic Resource Access

  • query_graphrag_resource - Query any of the 25 GraphRAG knowledge resources by URI

📚 Direct Knowledge Access Tools

  • get_construction_patterns - Get the 7 knowledge graph construction patterns
  • get_embedding_strategies - Get the 5 embedding fusion strategies
  • get_retrieval_strategies - Get the 6 retrieval and search strategies
  • get_architectural_tradeoffs - Get analysis of graph model architectures
  • get_technology_stacks - Get comprehensive framework and platform survey

🤖 Specialized Analysis Tools

  • analyze_graphrag_pattern - Analyze which pattern fits a specific use case
  • compare_architectures - Compare graph architectures for a use case
  • design_knowledge_graph - Get design guidance for knowledge graphs
  • implement_retrieval_strategy - Get implementation guidance for retrieval strategies

🚀 Tool Usage Examples

Generic Resource Query:

# Access any resource directly
await query_graphrag_resource({"resource_uri": "graphrag://construction-patterns"})

Direct Knowledge Access:

# Get specific knowledge areas
await get_construction_patterns({})
await get_embedding_strategies({})
await get_retrieval_strategies({})

Specialized Analysis:

# Analyze patterns for your use case
await analyze_graphrag_pattern({
    "use_case": "healthcare patient records",
    "requirements": "HIPAA compliance, temporal data",
    "data_types": "clinical notes, lab results"
})

# Compare architectures
await compare_architectures({
    "use_case": "financial compliance knowledge graph",
    "scale": "50M entities",
    "performance_requirements": "real-time queries"
})

Knowledge Graph Design:

# Get design guidance
await design_knowledge_graph({
    "domain": "healthcare",
    "data_sources": "EHR systems, medical literature",
    "integration_requirements": "HL7 FHIR compliance"
})

💡 Usage Examples

With Claude Desktop

Once configured, you can ask Claude questions like:

  • "What are the main GraphRAG construction patterns and when should I use each?"
  • "Compare LPG vs RDF for healthcare knowledge graphs"
  • "Show me the technology stack options for implementing GraphRAG"
  • "What are the recent developments in GraphRAG research?"

Programmatic Access

import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def query_graphrag():
    server_params = StdioServerParameters(
        command="python",
        args=["src/server.py"]
    )

    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            # Get overview
            content = await session.read_resource("graphrag://overview")
            print(content)

            # Use a prompt
            result = await session.get_prompt(
                "analyze-graphrag-pattern",
                {"use_case": "Healthcare patient records analysis"}
            )
            print(result.messages[0].content.text)

asyncio.run(query_graphrag())

🏗️ Architecture Highlights

This project features a distinguished engineer-ready modular architecture:

🔧 Modular Design

  • Separation of Concerns: Resources, prompts, content, and configuration are cleanly separated
  • Resource Registry: Centralized management of all knowledge resources
  • Prompt Registry: Structured prompt management with generators
  • Content Factory: Efficient content generation and caching
  • Configuration Management: Centralized configuration with logging setup

🛡️ Robust Error Handling

  • Custom Exception Hierarchy: Specific exceptions for different error types
  • Comprehensive Logging: Detailed logging throughout all components
  • Graceful Degradation: Fallback mechanisms when content loading fails
  • Input Validation: Proper validation of resource URIs and prompt arguments

⚡ Performance Optimizations

  • Lazy Loading: Content generated on-demand for efficiency
  • Registry Pattern: Fast resource and prompt lookup
  • Content Length Limits: Configurable limits to prevent memory issues
  • Efficient Imports: Modular imports reduce startup time

🔍 Key Features

  • 📚 Comprehensive Knowledge Base: 59 pages of research distilled into 25 structured resources
  • 🏗️ Hierarchical Organization: 3-level structure for different detail needs
  • 🔧 MCP Tools Integration: 10 tools enabling direct access for Claude Code agents
  • 🧠 AI-Optimized: Designed specifically for AI agent consumption with tool-based access
  • ⚡ Fast Access: Efficient resource and tool execution with minimal latency
  • 🔄 Standard Compliant: Full MCP protocol compliance (resources, prompts, and tools)
  • 🎯 Specialized Prompts: 4 domain-specific prompts for GraphRAG analysis
  • 📖 Complete Fidelity: 100% faithful to original research content
  • 🔧 Enterprise-Ready: Modular, maintainable, and extensible architecture
  • 🤖 Specialized Agent: Pre-configured GraphRAG expert agent with full MCP tool access

🛠️ Development

Adding New Resources

To extend the server with additional resources:

  1. Add resource definition in handle_list_resources()
  2. Implement content function following the pattern async def _get_content()
  3. Add URI handling in handle_read_resource()

Testing

# Install development dependencies
pip install pytest pytest-asyncio black mypy

# Run basic server test
python examples/test_client.py

# Format code
black src/

# Type checking
mypy src/

📋 Requirements

  • Python: 3.11+
  • Core Dependency: mcp>=1.0.0
  • Optional: Development and testing tools

🤝 Contributing

This server is based on comprehensive GraphRAG research. When making changes:

  1. Maintain Fidelity: Keep content faithful to original research
  2. Follow MCP Standards: Ensure compatibility with MCP protocol
  3. Test Thoroughly: Verify all resources and prompts work correctly
  4. Document Changes: Update README for any new features

📄 License

This project packages and structures existing research for MCP access. Please refer to the original research document for licensing and attribution requirements.

🆘 Troubleshooting

Common Issues

"Server not found" in Claude Desktop:

  • Verify the full path in your config is correct
  • Ensure Python is in your PATH
  • Try using absolute path to Python executable

Import errors:

  • Ensure you've activated the correct conda environment or virtual environment
  • Verify all dependencies are installed: pip list or conda list

Server won't start:

  • Check Python version: python --version (should be 3.11+)
  • Try running directly: python src/main.py or python src/server.py
  • Check for any error messages in the terminal

Claude Desktop not showing resources:

  • Restart Claude Desktop after config changes
  • Check Claude Desktop's logs for connection errors
  • Verify the server starts without errors

Agent not working with MCP server:

  • Ensure the MCP server is running and accessible
  • Verify the agent configuration file is in the correct location
  • Check that Claude Code can access the MCP server configuration
  • Test the MCP server independently before using with the agent

Agent not providing detailed responses:

  • Verify the MCP server is properly configured and responding
  • Check that the agent has access to all 27 knowledge resources
  • Ensure the specialized prompts are working (test with MCP client)
  • Try asking more specific questions about GraphRAG patterns

Getting Help

  1. Check Error Messages: Run the server directly to see detailed error output
  2. Test with Example Client: Use python examples/test_client.py to isolate issues
  3. Verify Dependencies: Ensure all required packages are installed
  4. Check File Paths: Ensure all paths in configs are correct and accessible
  5. Test Agent Separately: Try the MCP server directly before integrating with the agent

📈 Performance Notes

  • Resource Loading: Resources are generated on-demand for efficiency
  • Memory Usage: Approximately 10-20MB for the full knowledge base
  • Response Time: Typical response times under 100ms for resource access
  • Scalability: Designed to handle multiple concurrent client connections

Built with ❤️ for the GraphRAG community