Skip to main content
Glama
Sign In
Markdown RAG MCP

Markdown RAG MCP

by mohllal
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Markdown RAG MCP

A Retrieval-Augmented Generation (RAG) MCP server for markdown documentation with semantic search capabilities.

🎯 Core Capabilities

  • Document Indexing: Process markdown files with YAML frontmatter support, automatic chunking, and metadata extraction

  • Semantic Search: Find relevant content using natural language queries with configurable similarity thresholds

  • Incremental Updates: Change detection and indexing for large document collections

  • Real-time Monitoring: Automatic file system monitoring with live index updates

  • Advanced Embeddings: HuggingFace sentence-transformers with local model execution

  • Vector Storage: High-performance Milvus vector database with Docker Compose setup

  • CLI Interface: Beautiful command-line tools with progress tracking and interactive demos

Related MCP server: docrag

🤖 MCP Server Integration

This system is designed as an MCP server, providing a search tool with semantic search functionality accessible via MCP protocol.

🏗️ Architecture

For the full system architecture and components overview, check the Architecture Guide.

🚀 Quick Start

Prerequisites

  • Python 3.12+

  • Docker and Docker Compose

Installation

  1. Clone and setup:

    git clone <repository-url>
cd markdown-rag-mcp

  2. Start Milvus database:

    docker-compose -f docker/docker-compose.yml up -d

  3. Install dependencies using uv

    uv sync

  4. Install the package:

    pip install -e .

Basic Usage

CLI Interface

# Index documents (with optional monitoring)
markdown-rag-mcp index ./documents --recursive --watch

# Semantic search with confidence scoring
markdown-rag-mcp search "authentication setup" --limit 5

# System health monitoring
markdown-rag-mcp status

For the full overview of the CLI interface, check the CLI Guide.

Demo Scripts

# Experience incremental indexing with performance metrics
python examples/incremental_indexing_demo.py --setup --runs 5

# Complete RAG pipeline demonstration
python examples/milvus_embeddings_demo.py

For the full list of demo scripts, check the Examples Guide.

🔧 Configuration

Configure via environment variables or .env file, you can use .env.example for some defaults:

# Vector Database Configuration
MARKDOWN_RAG_MCP_MILVUS_HOST=localhost
MARKDOWN_RAG_MCP_MILVUS_PORT=19530
MARKDOWN_RAG_MCP_COLLECTION_NAME=markdown_docs

# Embedding Model Settings
MARKDOWN_RAG_MCP_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
MARKDOWN_RAG_MCP_EMBEDDING_DEVICE=auto  # cpu, cuda, mps, auto
MARKDOWN_RAG_MCP_EMBEDDING_DIMENSIONS=384

# Search and Processing
MARKDOWN_RAG_MCP_SIMILARITY_THRESHOLD=0.7
MARKDOWN_RAG_MCP_CHUNK_SIZE_LIMIT=1000
MARKDOWN_RAG_MCP_CHUNK_OVERLAP=200
MARKDOWN_RAG_MCP_MAX_CONCURRENT_INDEXING=2

# File Monitoring
MARKDOWN_RAG_MCP_WATCH_DEBOUNCE_SECONDS=2
MARKDOWN_RAG_MCP_WATCH_PATTERNS="**/*.md,**/*.markdown"

📁 Project Structure

markdown-rag-mcp/
├── src/markdown_rag_mcp/         # Core library implementation
│   ├── cli/                      # Command-line interface
│   ├── config/                   # Configuration management
│   ├── core/                     # RAG engine and interfaces
│   ├── embeddings/               # Embedding providers
│   ├── indexing/                 # Document processing pipeline
│   ├── models/                   # Data models and schemas
│   ├── monitoring/               # File system monitoring
│   ├── parsers/                  # Markdown and frontmatter parsing
│   ├── search/                   # Query processing and search
│   └── storage/                  # Vector database integration
├── tests/                        # Comprehensive test suite
├── examples/                     # Demo scripts
├── docker/                       # Docker Compose configuration
├── specs/                        # Technical specifications
└── documents/                    # Markdown documents for indexing and searching

🧪 Testing

To run the test suite, use the following commands:

# Run complete test suite
uv sync --all-extras
pytest

# Run specific component tests
pytest tests/indexing/ -v
pytest tests/search/ -v
pytest tests/embeddings/ -v

📚 Documentation

📄 License

MIT License - see LICENSE file for details.

Built with ❤️ for developers who need intelligent, markdown-based document search capabilities

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    This server cannot be deployed

    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    Maintenance

    ActivityInactive
    ResponsivenessOutdated data

    Resources

    Related MCP Connectors

    Related MCP Servers

    • MCP Documentation Server

      McKhanster
      F
      license
      Not graded
      quality
      D
      maintenance
      Enables semantic search and retrieval of MCP (Model Context Protocol) documentation using Redis-backed embeddings, allowing users to query and access documentation content through natural language.
      -

    • docrag

      ryan-m-bishop
      A
      license
      Not graded
      quality
      D
      maintenance
      Provides RAG (Retrieval Augmented Generation) access to technical documentation through MCP, enabling LLMs to search and retrieve relevant documentation on-demand.
      5
      MIT

    • doc-mcp

      tirth1263
      F
      license
      Not graded
      quality
      D
      maintenance
      Enables semantic search and AI-powered Q&A over ingested GitHub documentation repositories via MCP tools.
      -