RAGFlow MCP Server

RAGFlow MCP Server

A comprehensive Model Context Protocol (MCP) server for RAGFlow that provides full API access for semantic retrieval and knowledge base management.

Features

• Semantic Retrieval: Search across datasets using natural language queries
• Dataset Management: Create, list, update, and delete datasets
• Document Management: Upload, parse, list, download, and delete documents
• Chunk Management: Add, list, update, and delete document chunks
• Chat Assistants: Create and manage chat assistants with RAG capabilities
• Session Management: Create and manage chat sessions
• GraphRAG & RAPTOR: Build and query knowledge graphs (when supported by your RAGFlow instance)

Installation

Prerequisites

• Python 3.10+
• RAGFlow server running and accessible (v0.16.0+ for core features)
• RAGFlow API key

Note: GraphRAG and RAPTOR build APIs require RAGFlow v0.21.0 or later.

Install from source

git clone https://github.com/Juxsta/ragflow-mcp.git
cd ragflow-mcp
pip install -e .

Configure Claude Code

Add to your Claude Code MCP settings:

claude mcp add ragflow -e RAGFLOW_API_KEY=your-api-key -e RAGFLOW_URL=http://localhost:9380/api/v1 -- python -m ragflow_mcp.server

Or manually add to ~/.claude/settings.json:

{
  "mcpServers": {
    "ragflow": {
      "command": "python",
      "args": ["-m", "src.server"],
      "cwd": "/path/to/ragflow-mcp",
      "env": {
        "RAGFLOW_API_KEY": "your-api-key",
        "RAGFLOW_URL": "http://localhost:9380/api/v1"
      }
    }
  }
}

Environment Variables

Available Tools

Retrieval

• ragflow_retrieval_tool - Semantic search across datasets

Dataset Management

• ragflow_list_datasets_tool - List all datasets
• ragflow_create_dataset_tool - Create a new dataset
• ragflow_update_dataset_tool - Update dataset configuration
• ragflow_delete_dataset_tool - Delete a dataset (requires confirmation)

Document Management

• ragflow_list_documents_tool - List documents in a dataset
• ragflow_upload_document_tool - Upload a document (file path or base64)
• ragflow_parse_document_tool - Trigger async document parsing
• ragflow_parse_document_sync_tool - Parse and wait for completion
• ragflow_download_document_tool - Download document content
• ragflow_delete_document_tool - Delete a document (requires confirmation)
• ragflow_stop_parsing_tool - Cancel an active parsing job

Chunk Management

• ragflow_list_chunks_tool - List chunks in a document
• ragflow_add_chunk_tool - Add a chunk to a document
• ragflow_update_chunk_tool - Update chunk content/keywords
• ragflow_delete_chunk_tool - Delete chunks (requires confirmation)

Chat & Sessions

• ragflow_list_chats_tool - List chat assistants
• ragflow_create_chat_tool - Create a chat assistant
• ragflow_update_chat_tool - Update chat configuration
• ragflow_delete_chat_tool - Delete a chat assistant (requires confirmation)
• ragflow_list_sessions_tool - List sessions for a chat
• ragflow_create_session_tool - Create a new session
• ragflow_chat_tool - Send a message and get a response

GraphRAG & RAPTOR

• ragflow_build_graph_tool - Build knowledge graph for a dataset
• ragflow_graph_status_tool - Check graph construction status
• ragflow_get_graph_tool - Retrieve the knowledge graph
• ragflow_delete_graph_tool - Delete a knowledge graph (requires confirmation)
• ragflow_build_raptor_tool - Build RAPTOR tree for a dataset
• ragflow_raptor_status_tool - Check RAPTOR construction status

Usage Examples

Semantic Search

Query: "What is the main character's motivation?"
Dataset: your-dataset-id

Upload and Parse a Document

1. Upload: ragflow_upload_document_tool(dataset_id, file_path="/path/to/doc.pdf")
2. Parse: ragflow_parse_document_sync_tool(document_id)
3. Search: ragflow_retrieval_tool(query="your question", dataset_ids=[dataset_id])

Development

Run Tests

pip install -e ".[dev]"
pytest tests/ -v

Project Structure

ragflow-mcp/
├── src/
│   ├── __init__.py
│   ├── server.py          # FastMCP server setup
│   ├── connector.py       # RAGFlow API client
│   ├── config.py          # Configuration management
│   ├── cache.py           # LRU cache implementation
│   └── tools/
│       ├── retrieval.py   # Semantic search
│       ├── datasets.py    # Dataset CRUD
│       ├── documents.py   # Document management
│       ├── chunks.py      # Chunk management
│       ├── chat.py        # Chat & sessions
│       └── graph.