MCP server exposing RepoDigest's AST signatures, call graph, and token-budgeted context packing
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# repodigest-mcp **Token-efficient code context for AI coding assistants: Python signatures, call graphs and budget-packed context, served locally over MCP.** [](LICENSE) [](pyproject.toml) [](https://modelcontextprotocol.io) [](#testing) `repodigest-mcp` is a [Model Context Protocol](https://modelcontextprotocol.io) server that wraps [RepoDigest](https://github.com/nagendra-kon/repodigest)'s static analysis (AST parsing, call graph, BM25 search, token-budgeted packing) so that Claude Code, Cursor, or any MCP client can ask for *exactly the slice of a codebase it needs* instead of reading whole files. --- ## The problem Coding assistants explore a repository by reading files. To learn what one function accepts and returns, the assistant pulls in the entire file: every other function, every body, every import. On a real module that is routinely 90%+ noise for the question being asked, and it compounds over a session: - **Context bloat.** The window fills with code that doesn't matter, leaving less room for the code that does. - **Cost and latency.** You pay for every token in, on every subsequent turn. - **Worse answers.** Relevant details get buried in long contexts. Most of what an assistant needs is *structural*, and structure can be extracted deterministically. A function's interface is its signature and docstring. Its blast radius is its callers and callees. The code relevant to a task is a neighbourhood in the call graph around the best-matching symbol. None of that needs a model; it needs an AST. `repodigest-mcp` exposes those three operations as MCP tools. It runs as a local stdio subprocess: no daemon, no cloud service, no telemetry, and your source is parsed on your machine…
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