MCP Codebase RAG Server

Self-hosted MCP server that adds semantic vector search over your local codebases to any MCP-capable client (GitHub Copilot, Cline, Claude Desktop, etc.).
Goal: Robust RAG for Copilot (or any MCP client) without paying for Cursor/Windsurf.
Zero cost. Zero limits. Full control.

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📋 Overview

Problem Solved

• GitHub Copilot Pro has an excellent model but limited codebase RAG
• Cursor/Windsurf have good RAG but cost $15–20/month
• Continue.dev has RAG but doesn't integrate natively with MCP-aware agents

Solution

This MCP server provides:

1. Indexing of local codebases using vector embeddings
2. Hybrid semantic search via search_codebase tool — combines dense (embeddings) + sparse (BM25) + RRF fusion
3. Multi-project support with isolated ChromaDB collections
4. Universal integration with any MCP client

Tech Stack

Component	Technology	Why
Embeddings	sentence-transformers (all-MiniLM-L6-v2)	Fast, lightweight, 384-dim
Code embeddings	microsoft/unixcoder-base (optional)	Code-specific model, activated via EMBEDDING_MODEL
Vector DB	ChromaDB	Simple, persistent, zero config
Code parsing	Tree-sitter + BM25 + RRF	Universal language-agnostic chunking and hybrid search
MCP SDK	modelcontextprotocol/python-sdk	Official standard
Runtime	Python 3.11+	—

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🚀 Installation

Prerequisites

• Python 3.11+
• pip or uv

Install

git clone https://github.com/di5rupt0r/codebase-rag.git
cd codebase-rag

# Install as a package (adds the `codebase-rag` command to ~/.local/bin)
pip install -e .

Health Check

python scripts/health_check.py

Expected output:

🔍 MCP Codebase RAG Server Health Check
==================================================
Checking Embedding Provider... ✓ OK (3.03s)
Checking ChromaDB Connection... ✓ OK (0.14s)
Checking Search Functionality... ✓ OK (2.36s)
Checking Data Directory... ✓ OK (0.00s)
==================================================
Health Check Summary: 4/4 checks passed
🎉 All systems operational!

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📖 Quick Start

1. Index a Project

# Index the current directory
python scripts/index_project.py . --name my-project

# Index a specific path
python scripts/index_project.py ~/projects/api --name api-backend

# Force full reindex
python scripts/index_project.py . --name my-project --force

# Dry run to preview what will be indexed
python scripts/index_project.py . --name my-project --dry-run

2. Start the MCP Server

stdio (default — for local clients)

codebase-rag

HTTP (for remote clients or always-on service)

MCP_TRANSPORT=streamable-http MCP_PORT=8080 codebase-rag

3. Configure Your MCP Client

VS Code (GitHub Copilot / Cline) — stdio mode

Add to your VS Code mcp.json:

{
  "servers": {
    "codebase-rag": {
      "type": "stdio",
      "command": "codebase-rag"
    }
  }
}

VS Code — HTTP mode (when running as a service)

{
  "servers": {
    "codebase-rag": {
      "type": "http",
      "url": "http://127.0.0.1:8080/mcp"
    }
  }
}

Claude Desktop

{
  "mcpServers": {
    "codebase-rag": {
      "command": "codebase-rag"
    }
  }
}

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� Search Capabilities

Hybrid Search Architecture

The server implements a hybrid search system that combines:

1. Dense Search (Vector Embeddings)

   • Semantic similarity using sentence-transformers
   • Finds conceptually similar code
   • Base: ChromaDB vector similarity

2. Sparse Search (BM25)

   • Exact lexical term matching
   • Finds precise identifiers and keywords
   • Base: rank-bm25 with regex tokenization

3. Reciprocal Rank Fusion (RRF)

   • Intelligent fusion of dense + sparse results
   • k=60 (standard literature value)
   • Improves both precision and recall

Search Results

{
  "results": [
    {
      "path": "src/auth.py",
      "content": "def authenticate_user(user, password): ...",
      "score": 0.0325,
      "type": "function",
      "name": "authenticate_user", 
      "line_start": 15,
      "line_end": 25
    }
  ],
  "total_indexed_chunks": 1247,
  "query_time_ms": 23.4,
  "search_type": "hybrid_rrf"
}

Performance Characteristics

Metric	Target	Description
Tree-sitter parsing	< 50ms/file	Universal language parsing
BM25 indexing	< 10ms/query	In-memory reconstruction
RRF fusion	< 1ms	In-memory score calculation
Total query time	< 100ms	End-to-end hybrid search
Memory overhead	< 50MB	For 5k chunks

Fallback Behavior

• Tree-sitter unavailable → Line-based chunking
• BM25 unavailable → Dense-only search
• Both unavailable → Original dense search with keyword reranking

�️ MCP Tools

search_codebase

Hybrid semantic search over an indexed project using vector embeddings + BM25 + RRF fusion.

Input:

{
  "query": "where is the authentication logic?",
  "top_k": 5,
  "project": "my-project",
  "file_types": [".py", ".js"]
}

Output:

{
  "results": [
    {
      "path": "src/auth.py",
      "content": "def authenticate_user(user, password):\n    ...",
      "score": 0.89
    }
  ],
  "total_indexed_chunks": 1247,
  "query_time_ms": 23
}

reindex_project

Re-index a project after large changes.

Input:

{
  "project_path": "/path/to/your/project",
  "project_name": "my-project",
  "force": false
}

list_indexed_projects

List all indexed projects.

get_files

List indexed files in a project.

Input: { "project": "my-project" }

get_file_content

Return the full content of an indexed file.

Input: { "path": "src/main.py" }

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⚙️ Configuration

Environment Variables

# ChromaDB path (default: ./data/chroma_db relative to install dir)
export CHROMA_DB_PATH="/custom/path/to/chroma"

# Embedding model (default: all-MiniLM-L6-v2)
# Use microsoft/unixcoder-base for better code-specific embeddings (~2GB, requires torch)
export EMBEDDING_MODEL="microsoft/unixcoder-base"

# HTTP transport settings (only needed in HTTP/service mode)
export MCP_TRANSPORT="streamable-http"
export MCP_HOST="127.0.0.1"
export MCP_PORT="8080"
# Set this when exposing via reverse proxy or Tailscale Funnel
export MCP_ALLOWED_HOST="your-hostname.example.com"

# Log level (default: INFO)
export LOG_LEVEL="DEBUG"

Chunking (Advanced)

Edit src/codebase_rag/config.py:

CHUNK_SIZE = 500          # characters per chunk
CHUNK_OVERLAP = 50        # overlap between chunks
DEFAULT_TOP_K = 5         # default results per search

Supported File Types

Python, JavaScript, TypeScript, JSX, TSX, Java, C, C++, Go, Rust, Ruby, PHP, C#, Shell, YAML, JSON.

Ignored Patterns

*.pyc, __pycache__, .git, node_modules, .venv, venv, *.egg-info, .pytest_cache

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📊 Benchmarks

Operation	Expected Time	Notes
Index 20 .py files (~5k LOC)	~5–8s	First run; incremental is much faster
Vector search (top_k=5)	~20–50ms	ChromaDB in-process
Query embedding	~10–20ms	sentence-transformers, CPU
Server cold start	~2–3s	Model loaded into memory

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🤖 Automation Scripts

Auto-discovery

Scan a directory for Git repositories and index them all automatically:

python scripts/auto_index.py ~/projects

Watch Mode

Watch a project for file changes and reindex incrementally (debounced, 5s):

python scripts/watch.py /path/to/project --name my-project

Git Hook (post-commit reindex)

Install a post-commit hook so changed files are reindexed automatically after every commit:

python scripts/setup_git_hook.py /path/to/your/repo my-project

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🧪 Tests

# All tests (116 passing)
pytest -v

# Specific modules
pytest tests/test_config.py -v
pytest tests/test_embeddings.py -v
pytest tests/test_indexer.py -v
pytest tests/test_server.py -v

# With coverage
pytest --cov=codebase_rag --cov-report=html

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🔧 Deploy as a systemd Service (Linux)

A template service file is provided at systemd/codebase-rag-server.service.
Replace YOUR_USERNAME with your actual Linux username before installing:

# Substitute your username in-place
sed -i "s/YOUR_USERNAME/$USER/g" systemd/codebase-rag-server.service

# Install and start
sudo cp systemd/codebase-rag-server.service /etc/systemd/system/
sudo systemctl daemon-reload
sudo systemctl enable codebase-rag-server
sudo systemctl start codebase-rag-server

# Check
sudo systemctl status codebase-rag-server
sudo journalctl -u codebase-rag-server -f

Exposing Remotely via Tailscale Funnel (optional)

To use the server from a remote machine (Codespaces, company laptop, etc.):

# Expose port 8080 via Tailscale Funnel
tailscale funnel 8080

# Add to your service file:
# Environment="MCP_ALLOWED_HOST=your-machine.your-tailnet.ts.net"

# Then in your remote mcp.json:
# "url": "https://your-machine.your-tailnet.ts.net/mcp"

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🐛 Troubleshooting

Slow first start: The embedding model (~100MB) is downloaded on first use. Run health_check.py to pre-load it.

High memory usage: The default model uses ~500MB RAM. If needed, use an even smaller model via EMBEDDING_MODEL.

Permission errors: Ensure the running user has write access to data/chroma_db/.

Debug mode:

LOG_LEVEL=DEBUG codebase-rag

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📝 Contributing

1. Fork the project
2. Create a feature branch: git checkout -b feature/your-feature
3. Follow strict TDD: RED → GREEN → REFACTOR
4. Atomic, descriptive commits
5. Open a pull request with tests

# Dev setup
pip install -e ".[dev]"
pytest -v --cov=codebase_rag

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📄 License

MIT License — see LICENSE.

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🔗 References

• MCP Protocol
• ChromaDB Docs
• sentence-transformers
• FastMCP

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