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Graphlens MCP
Overview
Description from the maker (original English)
Semantic code graph MCP server for coding agents
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiVery new↓ 27/wk · ★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Developer tools
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- The registry lists no key to fill in.
You need
- PyPIPython’s uv installed (for the uvx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"graphlens": {
"command": "uvx",
"args": [
"graphlens-mcp"
]
}
}
}Run this in your terminal
claude mcp add graphlens -- uvx graphlens-mcpOr copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"graphlens": {
"command": "uvx",
"args": [
"graphlens-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"graphlens": {
"type": "stdio",
"command": "uvx",
"args": [
"graphlens-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account Neko1313, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Developer toolsi
- More stars than 0% of the other 4929 servers in this category
- Published to the registry more recently than 23% of the other 4929 servers in this category
- Basic checks equal to or better than 100% of the other 4929 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Graphlens MCP safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.4.0) reached the registry 4 months ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: GitHub, Desktop Commander, Cloudflare MCP
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Desktop Commander
MCP server for terminal commands, file operations, and process management
Runs on your computer
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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: https://neko1313.github.io/graphlens-mcp/
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.
📊 Results (10 repos · 3 models, strong → genuinely weak · ~2,400 graded runs —
full breakdown, significance tests and reproduction steps at
| | SIMPLE accuracy | HARD accuracy | HARD tokens (median) | HARD completion |
|---|---|---|---|---|
| graphlens | 0.980 – 1.000 | 0.899 – 0.921 | 22.4k – 34.1k | ≥ 0.959 on every model |
| codegraph | 0.912 – 0.990 | 0.655 – 0.939 | 23.2k – 70.0k | drops to 0.765 on the weakest model |
| semble | 0.647 – 0.961 | 0.555 – 0.850 | 21.6k – 74.9k | drops to 0.688 on the weakest model |
| none (control) | 0.366 – 0.681 | 0.453 – 0.685 | 0.1k – 0.9k | — |
Accuracy alone hides the number that matters to a bill: tokens paid per task. graphlens's
HARD-tier token spend stays flat (22k–34k) whether the driving model is strong or weak;
codegraph's and semble's balloon past 70k on the weakest model — more than double
graphlens's ceiling — for a *worse* answer, not a better one. graphlens is the only arm that
stays clearly ahead of the no-tools control and keeps completion above 0.95 at every
model tier: on the weakest model tested (gpt-oss-20b) it holds 0.900 HARD accuracy at
roughly half the token cost of codegraph. Pairwise Wilcoxon signed-rank tests (matched by
task) confirm the gap is statistically significant on the weaker models, not an artifact of a
few outlier tasks — see the notebook for per-model p-values and effect sizes.
Install
Requires Python ≥ 3.13 (a constraint inherited from graphlens).
uv tool install graphlens-mcp # or: pipx install graphlens-mcpPython language analysis works out of the box (the ty type engine ships as a dependency). Other languages parse immediately and unlock full cross-file semantics once their toolchain is present (Node for TypeScript, the Go toolchain, etc.); without it that language is reported as degraded rather than blocking init.
Quickstart (two commands)
uv tool install graphlens-mcp # 1. installcd your-project && graphlens-mcp init # 2. index + configure your agent
init detects the project's languages, indexes the code into a local graph, writes the MCP server entry into your agent's config and installs the navigation skill. You do not run serve yourself — your agent launches it from the config. Restart the agent and ask it something like *"what breaks if I change the signature of create_order?"*.
Commands
| Command | What it does |
|---|---|
graphlens-mcp init | Detect languages → toolchain doctor → full index → configure agents → install skill |
graphlens-mcp serve | Start the MCP server over stdio. Launched by the agent, not by you |
graphlens-mcp status | Show detected languages, toolchain status, and graph size/freshness |
graphlens-mcp reindex | Force a full rebuild (e.g. after installing a new toolchain) |
graphlens-mcp remove | Deregister from agents and (with --purge-db) delete the local graph |
Useful init flags: --root , --agent claude_code --agent cursor (repeatable), --no-agent, --no-skills, --db .
The graph lives at /.graphlens/graph.db (SQLite). It is a regenerable cache — safe to delete; reindex rebuilds it. Add .graphlens/ to your VCS ignore (the bundled init flow assumes it is not committed).
Supported languages
| Language | Engine | Out-of-box |
|---|---|---|
| Python | ty (bundled) | Full semantics immediately |
| TypeScript | Node bridge | degraded without Node; full semantics with Node installed |
| Go | Go toolchain | degraded without toolchain |
| Rust | SCIP / rust-analyzer | degraded without toolchain |
| PHP | PHP parser | degraded without toolchain |
graphlens-mcp status reports the actual resolver status per language. When a toolchain is missing, that language is reported as degraded (parsed structure, calls/types not fully resolved) with an install hint — it never blocks init.
Agent tools
Three tools — everything a symbol or file needs comes back as a navigable graph node, not a dead grep line. Each response carries a graph-quality status (ok | degraded) so the agent never mistakes a partial answer for a complete one, plus an indexing flag (true when a background reindex is running, so edges may be temporarily incomplete).
| Tool | Purpose |
|---|---|
search | Find code by NAME, CONTENT, or MEANING — the one way in. Returns graph nodes with their signature (often enough to answer without a follow-up call). Content is matched literally, not as a regex. Scope with path_glob (e.g. "tests/*", "*.ts", "!tests/*" to exclude a subtree); set exhaustive=true to list every matching file (no cap, no signatures) instead of the ranked top-N |
relations | A symbol's neighbourhood in one call: callers, callees, implementors/subclasses, and non-call references — each with its signature. The impact-analysis tool ("what breaks if I change X?", "what implements X?") |
info | Read a specific target: a symbol (node id or name) → source + signature + location; a file path → its symbol outline |
search and relations accept either a symbol name or a node id directly — you don't need to look up a node id first. Both cap their response size (a large hit set is ranked by relevance via a small bundled embedding model, not just truncated) and surface true counts (callers_total, references_total, …) when a list is capped, so the agent sees "15 shown of 22" instead of guessing. If the embedding model can't be fetched (e.g. a first run with no network), search transparently falls back to name/content matching.
Freshness model
A single mechanism keeps the graph current: a filesystem watcher (serve starts it by default; disable with --no-watch). When a file changes on disk the server re-indexes the connected set — the changed file plus the files that import it and the files it imports — with one full analyze, so cross-file edges are rebuilt correctly rather than left partial. Deleting a file prunes its symbols and refreshes its importers. There is no polling and no structure-only "skeleton" phase: every (re)index produces the full graph the resolver can give. As a backstop, a tool that touches a file the watcher hasn't processed yet triggers the same connected re-index on access.
Files created, deleted or edited *while the server was down* are invisible to an event-based watcher, so serve runs a one-shot reconcile at startup: it scans the project, indexes new files, prunes vanished ones, and refreshes any that changed — then hands off to the watcher.
Known limitations
- Connected-set re-link, deep ripples: the watcher re-links the *connected set* of a change (the changed file plus its direct importers and imports), not the entire project. A rename that ripples through many indirection layers may need a full
reindexfor an exact graph. Creating a file that an *unchanged* file already imports is handled — a second importer pass re-links that importer once the new file is indexed. - Cross-language edges on incremental edits: synthesized
COMMUNICATES_WITHedges are re-synthesized for every boundary a re-indexed file touches, so a new or moved exposer/consumer is linked without a fullreindex. A change that leaves a boundary entirely (a file that stops exposing an endpoint others still consume) may still need a fullreindexfor an exact cross-language view; the boundary-based query resolves connections regardless.
Uninstall
graphlens-mcp remove deregisters the server from your agents; add --purge-db to also delete the local .graphlens/ cache.
Development
uv sync --all-groups # install lint + test toolingtask check # ruff + format-check + ty + bandit + pytest (the CI gate) task docs:serve # preview the docs site locally (needs Node + pnpm)
See the [Architecture](https://neko1313.github.io/graphl
Only the beginning is shown; read the rest in the repo