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 : 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 viagit 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 searchget_chunk_code
: Retrieve specific chunks by IDfind_similar_chunks
: Find similar chunksindex_workspace
: Manually trigger re-indexingget_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.
Supported: Go, JavaScript, Markdown, Python, TypeScript
Planned: C, C++, Java, Ruby, Rust, and others
Contributing
All contributions welcome! See CONTRIBUTING.md.
This server cannot be installed
local-only server
The server can only run on the client's local machine because it depends on local resources.
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.
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