Daniel LightRAG MCP Server

A comprehensive MCP (Model Context Protocol) server that provides full integration with LightRAG API, offering 22 tools across 4 categories for complete document management, querying, knowledge graph operations, and system management.

Features

• Document Management: 8 tools for inserting, uploading, scanning, retrieving, and deleting documents
• Query Operations: 2 tools for text queries with regular and streaming responses
• Knowledge Graph: 7 tools for accessing, checking, updating, and deleting entities and relations
• System Management: 5 tools for health checks, status monitoring, and cache management

Quick Start

1. Install the server:

   pip install -e .
   

2. Start LightRAG server (ensure it's running on http://localhost:9621)

3. Configure your MCP client (e.g., Claude Desktop):

   {
     "mcpServers": {
       "daniel-lightrag": {
         "command": "python",
         "args": ["-m", "daniel_lightrag_mcp"]
       }
     }
   }
   

4. Test the connection: Use the get_health tool to verify everything is working.

Installation

# Basic installation
pip install -e .

# With development dependencies
pip install -e ".[dev]"

Usage

Command Line

Start the MCP server:

daniel-lightrag-mcp

Environment Variables

Configure the server with environment variables:

export LIGHTRAG_BASE_URL="http://localhost:9621"
export LIGHTRAG_API_KEY="your-api-key"  # Optional
export LIGHTRAG_TIMEOUT="30"            # Optional
export LOG_LEVEL="INFO"                 # Optional

daniel-lightrag-mcp

Configuration

The server expects LightRAG to be running on http://localhost:9621 by default. Make sure your LightRAG server is started before running this MCP server.

For detailed configuration options, see CONFIGURATION_GUIDE.md.

Available Tools (22 Total)

Document Management Tools (8 tools)

insert_text

Insert text content into LightRAG.

Parameters:

• text (required): Text content to insert

Example:

{
  "text": "This is important information about machine learning algorithms and their applications in modern AI systems."
}

insert_texts

Insert multiple text documents into LightRAG.

Parameters:

• texts (required): Array of text documents with optional title and metadata

Example:

{
  "texts": [
    {
      "title": "AI Overview",
      "content": "Artificial Intelligence is transforming industries...",
      "metadata": {"category": "technology", "author": "researcher"}
    },
    {
      "content": "Machine learning algorithms require large datasets..."
    }
  ]
}

upload_document

Upload a document file to LightRAG.

Parameters:

• file_path (required): Path to the file to upload

Example:

{
  "file_path": "/path/to/document.pdf"
}

scan_documents

Scan for new documents in LightRAG.

Parameters: None

Example:

{}

get_documents

Retrieve all documents from LightRAG.

Parameters: None

Example:

{}

get_documents_paginated

Retrieve documents with pagination.

Parameters:

• page (required): Page number (1-based)
• page_size (required): Number of documents per page (1-100)

Example:

{
  "page": 1,
  "page_size": 20
}

delete_document

Delete a specific document by ID.

Parameters:

• document_id (required): ID of the document to delete

Example:

{
  "document_id": "doc_12345"
}

clear_documents

Clear all documents from LightRAG.

Parameters: None

Example:

{}

Query Tools (2 tools)

query_text

Query LightRAG with text.

Parameters:

• query (required): Query text
• mode (optional): Query mode - "naive", "local", "global", or "hybrid" (default: "hybrid")
• only_need_context (optional): Whether to only return context without generation (default: false)

Example:

{
  "query": "What are the main concepts in machine learning?",
  "mode": "hybrid",
  "only_need_context": false
}

query_text_stream

Stream query results from LightRAG.

Parameters:

• query (required): Query text
• mode (optional): Query mode - "naive", "local", "global", or "hybrid" (default: "hybrid")
• only_need_context (optional): Whether to only return context without generation (default: false)

Example:

{
  "query": "Explain the evolution of artificial intelligence",
  "mode": "global"
}

Knowledge Graph Tools (7 tools)

get_knowledge_graph

Retrieve the knowledge graph from LightRAG.

Parameters: None

Example:

{}

get_graph_labels

Get labels from the knowledge graph.

Parameters: None

Example:

{}

check_entity_exists

Check if an entity exists in the knowledge graph.

Parameters:

• entity_name (required): Name of the entity to check

Example:

{
  "entity_name": "Machine Learning"
}

update_entity

Update an entity in the knowledge graph.

Parameters:

• entity_id (required): ID of the entity to update
• properties (required): Properties to update

Example:

{
  "entity_id": "entity_123",
  "properties": {
    "description": "Updated description for machine learning",
    "category": "AI Technology"
  }
}

update_relation

Update a relation in the knowledge graph.

Parameters:

• relation_id (required): ID of the relation to update
• properties (required): Properties to update

Example:

{
  "relation_id": "rel_456",
  "properties": {
    "strength": 0.9,
    "type": "implements"
  }
}

delete_entity

Delete an entity from the knowledge graph.

Parameters:

• entity_id (required): ID of the entity to delete

Example:

{
  "entity_id": "entity_789"
}

delete_relation

Delete a relation from the knowledge graph.

Parameters:

• relation_id (required): ID of the relation to delete

Example:

{
  "relation_id": "rel_101"
}

System Management Tools (5 tools)

get_pipeline_status

Get the pipeline status from LightRAG.

Parameters: None

Example:

{}

get_track_status

Get track status by ID.

Parameters:

• track_id (required): ID of the track to get status for

Example:

{
  "track_id": "track_abc123"
}

get_document_status_counts

Get document status counts.

Parameters: None

Example:

{}

clear_cache

Clear LightRAG cache.

Parameters: None

Example:

{}

get_health

Check LightRAG server health.

Parameters: None

Example:

{}

Example Workflows

Complete Document Management Workflow

1. Check server health:

   {"tool": "get_health", "arguments": {}}
   

2. Insert documents:

   {
     "tool": "insert_texts",
     "arguments": {
       "texts": [
         {
           "title": "AI Research Paper",
           "content": "Recent advances in transformer architectures have shown remarkable improvements in natural language understanding tasks...",
           "metadata": {"category": "research", "year": 2024}
         }
       ]
     }
   }
   

3. Query the knowledge base:

   {
     "tool": "query_text",
     "arguments": {
       "query": "What are the recent advances in transformer architectures?",
       "mode": "hybrid"
     }
   }
   

4. Explore the knowledge graph:

   {"tool": "get_knowledge_graph", "arguments": {}}
   

5. Check entity existence:

   {
     "tool": "check_entity_exists",
     "arguments": {"entity_name": "transformer architectures"}
   }
   

Knowledge Graph Management Workflow

1. Get current graph structure:

   {"tool": "get_knowledge_graph", "arguments": {}}
   

2. Get available labels:

   {"tool": "get_graph_labels", "arguments": {}}
   

3. Update entity properties:

   {
     "tool": "update_entity",
     "arguments": {
       "entity_id": "transformer_arch_001",
       "properties": {
         "description": "Advanced neural network architecture for sequence processing",
         "applications": ["NLP", "computer vision", "speech recognition"],
         "year_introduced": 2017
       }
     }
   }
   

4. Update relation properties:

   {
     "tool": "update_relation",
     "arguments": {
       "relation_id": "rel_improves_002",
       "properties": {
         "improvement_factor": 2.5,
         "confidence": 0.92,
         "evidence": "Multiple benchmark studies"
       }
     }
   }
   

System Monitoring Workflow

1. Check overall health:

   {"tool": "get_health", "arguments": {}}
   

2. Monitor pipeline status:

   {"tool": "get_pipeline_status", "arguments": {}}
   

3. Check document processing status:

   {"tool": "get_document_status_counts", "arguments": {}}
   

4. Track specific operations:

   {
     "tool": "get_track_status",
     "arguments": {"track_id": "upload_batch_001"}
   }
   

5. Clear cache when needed:

   {"tool": "clear_cache", "arguments": {}}
   

Error Handling

The server provides comprehensive error handling with detailed error messages:

• Connection Errors: When LightRAG server is unreachable
• Authentication Errors: When API key is invalid or missing
• Validation Errors: When input parameters are invalid
• API Errors: When LightRAG API returns errors
• Timeout Errors: When requests exceed timeout limits
• Server Errors: When LightRAG server returns 5xx status codes

All errors include:

• Error type and message
• HTTP status code (when applicable)
• Timestamp
• Tool name that caused the error
• Additional context data when available

Error Response Format

{
  "tool": "insert_text",
  "error_type": "LightRAGConnectionError",
  "message": "Failed to connect to LightRAG server at http://localhost:9621",
  "timestamp": 1703123456.789,
  "status_code": null,
  "response_data": {}
}

Common Error Scenarios

Connection Errors

{
  "error_type": "LightRAGConnectionError",
  "message": "Connection refused to http://localhost:9621",
  "status_code": null
}

Validation Errors

{
  "error_type": "LightRAGValidationError", 
  "message": "Missing required arguments for query_text: ['query']",
  "validation_errors": [
    {
      "loc": ["query"],
      "msg": "field required",
      "type": "value_error.missing"
    }
  ]
}

API Errors

{
  "error_type": "LightRAGAPIError",
  "message": "Document not found",
  "status_code": 404,
  "response_data": {
    "detail": "Document with ID 'doc_123' does not exist"
  }
}

Troubleshooting

Quick Diagnostics

1. Check LightRAG Server Status:

   curl http://localhost:9621/health
   

2. Test MCP Server:

   python -m daniel_lightrag_mcp &
   sleep 2
   pkill -f daniel_lightrag_mcp
   

3. Verify Installation:

   python -c "import daniel_lightrag_mcp; print('OK')"
   

Common Issues

Server Won't Start

• Check Python version: Requires Python 3.8+
• Verify dependencies: Run pip install -e .
• Check port availability: Ensure no conflicts on stdio

Connection Refused

• LightRAG not running: Start LightRAG server first
• Wrong URL: Verify LIGHTRAG_BASE_URL environment variable
• Firewall blocking: Check firewall settings for port 9621

Authentication Failed

• Missing API key: Set LIGHTRAG_API_KEY environment variable
• Invalid key: Verify API key with LightRAG server
• Key format: Ensure key format matches LightRAG expectations

Timeout Errors

• Increase timeout: Set LIGHTRAG_TIMEOUT=60 environment variable
• Check server load: Verify LightRAG server performance
• Network latency: Test direct API calls with curl

Tool Not Found

• Restart MCP client: Reload server configuration
• Check tool name: Verify exact tool name spelling
• Server registration: Ensure all 22 tools are listed

Debug Mode

Enable detailed logging:

export LOG_LEVEL=DEBUG
python -m daniel_lightrag_mcp

Getting Help

1. Check server logs for detailed error messages
2. Test individual tools with minimal examples
3. Verify LightRAG server is responding correctly
4. Review the Configuration Guide for setup details

Development

Install development dependencies:

pip install -e ".[dev]"

Run tests:

pytest

Run tests with coverage:

pytest --cov=src/daniel_lightrag_mcp --cov-report=html

Format code:

black src/ tests/
isort src/ tests/

License

MIT License