MCP Codebase RAG Server
Provides semantic vector search over local codebases for GitHub Copilot via MCP, enabling enhanced codebase RAG without additional cost.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Codebase RAG Serversearch codebase for error handling in user registration"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
๐ Overview
Problem Solved
GitHub Copilot Pro has an excellent model but limited codebase RAG
Cursor/Windsurf have good RAG but cost $15โ20/month
Continue.dev has RAG but doesn't integrate natively with MCP-aware agents
Solution
This MCP server provides:
Indexing of local codebases using vector embeddings
Hybrid semantic search via
search_codebasetool โ combines dense (embeddings) + sparse (BM25) + RRF fusionMulti-project support with isolated ChromaDB collections
Universal integration with any MCP client
Tech Stack
Component | Technology | Why |
Embeddings | sentence-transformers ( | Fast, lightweight, 384-dim |
Code embeddings | microsoft/unixcoder-base (optional) | Code-specific model, activated via |
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+ | โ |
Related MCP server: ragi
๐ Installation
Prerequisites
Python 3.11+
piporuv
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.pyExpected output:
๐ MCP Codebase RAG Server Health Check
==================================================
Checking Embedding Provider... โ OK (3.03s)
Checking ChromaDB Connection... โ OK (0.14s)
Checking Search Functionality... โ OK (2.36s)
Checking Data Directory... โ OK (0.00s)
==================================================
Health Check Summary: 4/4 checks passed
๐ All systems operational!๐ 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-run2. Start the MCP Server
stdio (default โ for local clients)
codebase-ragHTTP (for remote clients or always-on service)
MCP_TRANSPORT=streamable-http MCP_PORT=8080 codebase-rag3. Configure Your MCP Client
VS Code (GitHub Copilot / Cline) โ stdio mode
Add to your VS Code mcp.json:
{
"servers": {
"codebase-rag": {
"type": "stdio",
"command": "codebase-rag"
}
}
}VS Code โ 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"
}
}
}๏ฟฝ Search Capabilities
Hybrid Search Architecture
The server implements a hybrid search system that combines:
Dense Search (Vector Embeddings)
Semantic similarity using sentence-transformers
Finds conceptually similar code
Base: ChromaDB vector similarity
Sparse Search (BM25)
Exact lexical term matching
Finds precise identifiers and keywords
Base: rank-bm25 with regex tokenization
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 โ Line-based chunking
BM25 unavailable โ Dense-only search
Both unavailable โ Original dense search with keyword reranking
๏ฟฝ๏ธ 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" }
โ๏ธ 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 searchSupported 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
๐ Benchmarks
Operation | Expected Time | Notes |
Index 20 .py files (~5k LOC) | ~5โ8s | First run; incremental is much faster |
Vector search (top_k=5) | ~20โ50ms | ChromaDB in-process |
Query embedding | ~10โ20ms | sentence-transformers, CPU |
Server cold start | ~2โ3s | Model loaded into memory |
๐ค Automation Scripts
Auto-discovery
Scan a directory for Git repositories and index them all automatically:
python scripts/auto_index.py ~/projectsWatch Mode
Watch a project for file changes and reindex incrementally (debounced, 5s):
python scripts/watch.py /path/to/project --name my-projectGit 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-project๐งช 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=html๐ง 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 -fExposing 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"๐ 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-rag๐ Contributing
Fork the project
Create a feature branch:
git checkout -b feature/your-featureFollow strict TDD: RED โ GREEN โ REFACTOR
Atomic, descriptive commits
Open a pull request with tests
# Dev setup
pip install -e ".[dev]"
pytest -v --cov=codebase_rag๐ License
MIT License โ see LICENSE.
๐ References
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