MCP server for semantic code search and navigation that reduces token waste for AI agents.
sourcerer-mcp is worth checking the docs before setup with strong trust signals. Check agent compatibility and use-case fit before adding it to your workflow.
gh repo view st3v3nmw/sourcerer-mcp --webOpen the official README and confirm the supported install method.
Add the server entry to your MCP client config.
Restart your agent and verify that the server tools appear.
Repository setup guidance
Strong trust signals; still review the README and permissions before production use.
Last commit was about 304 days ago.
115 GitHub stars indicate community interest.
1 open issues signal maintenance load.
MIT license detected.
3 security/trust notes recorded.
Setup difficulty is 3/5.
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Sourcerer MCP helps AI agents find the right code without reading entire files. It builds a search index of your codebase so agents can jump directly to functions or classes they need. This saves tokens and speeds up development.
Sourcerer MCP is a Model Context Protocol server that enables AI agents to perform semantic code search and navigation. It parses source code using Tree-sitter, extracts meaningful chunks (functions, classes, methods, types), generates embeddings via OpenAI's API, and stores them in a persistent vector database. The server provides MCP tools such as semantic_search, get_chunk_code, find_similar_chunks, index_workspace, and get_index_status. It watches for file changes and automatically re-indexes modified files. Supported languages include Go, JavaScript, Markdown, Python, and TypeScript, with more planned. This approach dramatically reduces token usage by allowing agents to find relevant code without reading entire files.
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MCP server for semantic code search and navigation that reduces token waste for AI agents.
Developers using AI coding assistants like Claude Code, Teams working on large codebases needing efficient code search, Open source maintainers looking to improve code discoverability
The setup section provides repository-level starting guidance, not a guarantee of an independently verified installation. Check the official README and release notes.
Requires OpenAI API key; embeddings are sent to OpenAI servers. Stores vector database locally in .sourcerer/ directory; ensure it is added to .gitignore.
Similar or complementary options to evaluate include metorial, mcp-context-forge, Microsoft Learn MCP Server.
Aug 20, 2026