st3v3nmw/sourcerer-mcp

An MCP server for semantic code search & navigation that helps AI agents work
efficiently without burning through costly tokens.
Instead of reading entire files, agents can search conceptually and
jump directly to the specific functions, classes, and code chunks they need.
Demo
Requirements
- **OpenAI API Key**: Required for generating embeddings (local embedding support planned)
- **Git**: Must be a git repository (respects .gitignore files)
- **Add .sourcerer/ to .gitignore**: This directory stores the embedded vector database
Installation
Go
Homebrew
Configuration
Claude Code
mcp.json
How it Works
Sourcerer 🧙 builds a semantic search index of your codebase:
1. Code Parsing & Chunking
- Uses Tree-sitter to parse source files into ASTs
- Extracts meaningful chunks (functions, classes, methods, types) with stable IDs
- Each chunk includes source code, location info, and contextual summaries
- Chunk IDs follow the format: file.ext::Type::method
2. File System Integration
- Watches for file changes using fsnotify
- Respects .gitignore files via git check-ignore
- Automatically re-indexes changed files
- Stores metadata to track modification times
3. Vector Database
- Uses chromem-go for persistent vector storage in .sourcerer/db/
- Generates embeddings via OpenAI's API for semantic similarity
- Enables conceptual search rather than just text matching
- Maintains chunks, their embeddings, and metadata
4. MCP Tools
- semantic_search: Find relevant code using semantic search
- get_chunk_code: Retrieve specific chunks by ID
- find_similar_chunks: Find similar chunks
- index_workspace: Manually trigger re-indexing
- get_index_status: Check indexing progress
This approach allows AI agents to find relevant code without reading entire files,
dramatically reducing token usage and cognitive load.
Supported Languages
Language support requires writing Tree-sitter queries to
identify functions, classes, interfaces, and other code structures for each language.