Graphlens MCP server

graphlens-mcp

<!-- mcp-name: io.github.Neko1313/graphlens-mcp -->

A free, MIT-licensed MCP server that gives coding agents (Claude Code, Cursor, and compatible clients) a semantic code graph of your project — symbols, cross-file calls, references, imports and cross-language boundaries.

Instead of reading files top-to-bottom or grepping for names, the agent navigates the structure: who calls this function, what does it depend on, what breaks if I change its signature. It is a thin runtime layer over the graphlens analysis engine: graphlens provides the mechanisms (parsing, stable node identity, resolvers); graphlens-mcp owns the storage, freshness and the agent-facing surface.

📖 Documentation:

Status: early. The core navigation works; see Known limitations.

Why

Coding agents discover structure the slow way — grep, glob, read one file at a time — rebuilding call paths by hand before the real work even starts. The motivation is the same as every other code-context tool: stop the agent from grepping. The approach is what sets graphlens apart.

Most tools answer this by building their own model of your code — an ad-hoc graph stitched from heuristics, where every tool maps the codebase a little differently and nothing is authoritative. graphlens takes the opposite bet: it builds on the language's own real analysis engines — rust-analyzer, gopls, the TypeScript compiler, the bundled ty type engine — the LSP-grade tooling the industry already trusts. That yields a stable, real picture of the project (who actually calls what, across files and languages), not a bespoke approximation. And a stable foundation is something you can build on: attach context to the parts of a change that matter, auto-extract semantic clusters, answer impact questions reliably.

That foundation is the graphlens engine — parsing, stable node identity, and the resolvers. graphlens-mcp is a smart, agent-facing layer over it, and — honestly — a worked example of how to use the engine: it persists the graph (so the whole thing isn't held in memory), adds a semantic + clustering layer on top, keeps it fresh as you edit, and exposes it to agents as navigation tools plus a bundled skill. From that example it is growing into a self-sufficient system — one that, measured against the market's giants, aims for stable, reproducible results: better in some places, worse in others, but honest about which (see How it compares).

How it compares

graphlens-mcp ships with a reproducible A/B benchmark (benchmarks/) that drives the same agent against three interchangeable code-context MCP servers — graphlens, semble (semantic search), and codegraph (graph index) — plus a no-tools control that measures how much each server adds over the model's own memory. It runs across real Go / Rust / Python / TypeScript codebases and grades answers deterministically against oracle gold (no LLM judge), stratified into SIMPLE lookups vs HARD impact / cross-file questions, and reports accuracy alongside token / tool-call / dollar cost — because a cheaper arm at equal accuracy wins.

Shortened. The full README is on GitHub.