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PopNx MCPMCP servers for people who use AI · explained in plain words

CGraph

by Nxtsoftversion 0.9.7i★ –iRuns on your computer
Registry nameio.github.Nxtsoft/cgraph

Install

Overview

Description from the maker (original English)

Code graph for coding agents: symbol search, callers, blast radius and cross-service impact.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

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: Search & AI memory

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

  • npmNode.js installed (for the npx 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": {
    "cgraph": {
      "command": "npx",
      "args": [
        "-y",
        "cgraph-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add cgraph -- npx -y cgraph-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "cgraph": {
      "command": "npx",
      "args": [
        "-y",
        "cgraph-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.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "cgraph": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "cgraph-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 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account Nxtsoft, 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 Search & AI memoryi

  • More stars than 0% of the other 7226 servers in this category
  • Published to the registry more recently than 70% of the other 7226 servers in this category
  • Basic checks equal to or better than 100% of the other 7226 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 CGraph 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 (0.9.7) reached the registry 16 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 3 days ago
Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago
Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 44 days ago
Install

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.

English · 简体中文

*Scan a project · extract structure into a deterministic graph · query it in ~10 ms from a daemon, a thin client, or an MCP server.*

Contents

Why CGraph? · Architecture · Repository layout · What it produces · Output formats · Performance · Retrieval benchmark · Languages · Quick start · Install & Setup · Use with coding agents · CLI · daemon · MCP reference · Host integrations & enrichment · Development notes · Contributing · License

Why CGraph?

Reading a large repo to answer "what calls this?" or "what breaks if I change it?" means loading dozens of files — and those answers aren't in any single file. They live in the *relationships between* files. CGraph precomputes them so you (and your AI agent) navigate by graph, not grep.

🔗 Reverse dependencies & blast radiusgraph_impact returns every transitive dependent of a symbol in ~10 ms. On a real 10,706-node repo it surfaced 93 dependents of one god-file, instantly — an answer grep can't give you.
🧭 Navigate, don't grepCentrality-ranked search, node neighborhoods, shortest paths, and token-budgeted source bundles — served warm in ~10 ms instead of many file reads that burn context.
🏛️ Architecture at a glanceCentrality ranking surfaces the load-bearing files; Leiden/Louvain community detection clusters the repo into modules automatically.
⚡ Deterministic & fastA full 10k-node graph builds in ~2 s; no LLM in the extraction path — same input, same graph, every time.
🤖 Built for coding agentsA standard MCP server drops the graph straight into Claude / Codex, so they reason over structure instead of blindly reading files.

The pitch in one line: for a codebase too big to hold in your head, CGraph is the index that decides *which* code to read — ctags/"find all references", precomputed, centrality-ranked, and exposed over MCP.

Architecture

  • `cgraph` — one-shot scan → portable disk exports (graph.json, graph.html, graph.svg, obsidian.md, cypher.txt, modules.mmd, modules.svg, design.mmd, design.md).
  • `graphd` + `cgraph-client` — a resident per-project daemon with live file-watching; warm query / path / explain / impact / context in ~10 ms.
  • `cgraph-mcp` — a Model Context Protocol server so agents navigate the graph directly.

The engine owns deterministic extraction, fragment validation, cache state, and local graph mutation; hosts own model choice and semantic enrichment.

Repository layout

src/cli/          One-shot CLI entrypoint: cgraph

src/daemon/ Daemon entrypoint: graphd src/client/ Thin client runtime and cgraph-client executable src/mcp/ MCP request handling and cgraph-mcp executable src/engine/ Detection, extraction, graph building, analysis, daemon ops tests/smoke/ CTest smoke coverage for engine, daemon, MCP, and integration paths tests/fuzz/ Optional libFuzzer targets integrations/ Host hook and always-on integration scripts docs/ Host integration contract and benchmark notes vendor/ Vendored tree-sitter core and grammars

What it produces

A single scan turns a source tree into an interactive, explorable graph — communities colored, hubs sized by centrality:

The real graph.html viewer on a 10,708-node / 28,945-edge codebase — self-contained HTML, no external JS.

Output formats

  • graph.json — directed node-link JSON with graph metadata, nodes, and links. Node ids derive from each file's path relative to the project root, so the same commit built from two checkouts yields the same ids and two graphs can be joined by id; source_file stays the path the graph was read from
  • graph.html — browser-readable interactive graph view
  • graph.svg — static graph visualization
  • obsidian.md — markdown export for Obsidian-style navigation
  • cypher.txt — Neo4j Cypher statements
  • modules.mmd — module dependency diagram as Mermaid
  • modules.svg — module dependency diagram as a layered static image
  • design.mmd — entry points and their top call flows as a Mermaid flowchart TD
  • design.md — the same as Markdown: entry-point table, nested flows, layers by call distance

Performance

The interactive viewer used to run an O(N²) force simulation in the browser on every open. Layout is now precomputed once in C++ (via igraph), and repaints are viewport-culled — both dependency-free:

graphtime-to-settle beforeafterper-frame repaint
10,000 nodes35.2 s (main thread pinned)~0 ms (static layout)20–34× cheaper (zoom/pan)

Retrieval benchmark

CGraph has been scored on Agent Retrieval Bench: 427 samples from 25 repositories, each checked out at its base commit, scored with the benchmark's own metric code. The table covers all 345 positive samples.

MethodRecall@20MRRBCY@8k
CGraph bin-v0.9.1 (label + code-text search)0.63620.27870.4319
CGraph bin-v0.8.3 (label-only text search)0.57260.23560.3790
RepoMap (published)0.63330.21580.3788
RepoMap (rerun on this harness)0.63110.21560.3846
Qwen3-Embedding-8B (published)0.70290.23360.3732
Qwen3-Embedding-4B (published)0.63060.23790.3409
BM25 (published)0.44520.15200.2051

How bin-v0.9.1 compares:

  • Recall@20. Level with RepoMap (the difference is inside run-to-run noise of about 0.006). Below Qwen3-Embedding-8B. Above the other published single methods.
  • MRR. Higher than every published single method.
  • BCY@8k. Higher than every published single method, by more than the noise floor.
  • Against the paper's fusion of Qwen3-Embedding-8B and RepoMap, on the 287-sample core it reports: MRR is level (the fusion scores only its top 20; CGraph's MRR over its top 20 is 0.276 vs 0.271, inside the noise floor); Recall@20 (0.633 vs 0.733) and BCY@8k (0.440 vs 0.488) are lower.
  • Abstention. CGraph does not abstain on no-gold samples.

Per-task tables, pins, the noise floor and caveats are in bench/arb/RESULTS.md. Reproduce with bench/arb/run.sh.

Languages

Tree-sitter-backed structural extraction, with regex/structured extraction for a few config formats:

Plus structured/regex extraction for Apex, Delphi form/source, MSBuild/XML project files, and MCP config files.

Type members

Seven languages emit a field node per declared member, with a defines edge from the owner:

LanguageOwners with membersField properties
C, C++class, struct only — not enum or unionnone
TypeScript, TSXinterface, object type alias, class (constructor parameter properties included), enumtype_text, optional, readonly
Gostruct, including multi-name and embedded fieldstype_text
Rustnamed and tuple struct, union, enum variant, trait associated type and constanttype_text
Pythonclass-body assignment, including chained and tuple targetstype_text when annotated
Javaclass, record, enumtype_text, readonly

Members are declared members only: no inherited-member expansion, no alias flattening, no runtime attribute inference. A declaration that is already a node of its own — a TypeScript method_signature, a Rust trait method or type alias — keeps its function or type node and is not also a field. A field never takes the id a function or type holds; it moves instead, so a symbol's id stays stable.

A class-like declaration without a body (struct FileCacheEntry;) is a forward declaration and mints no node; only the body-bearing definition does.

Fields are members, not navigation targets: graph_context packs the owner (whose snippet spans its members) rather than the members, and report modules symbol counts exclude them.

HTTP endpoints

JavaScript and TypeScript services get one endpoint node per route the code serves, with the full path composed across files: an Elysia, Express or Hono chain's own prefix (new Elysia({ prefix: '/notebooks' }), .basePath('/v1')) beneath every .use(child), .use('/p', child) or .route('/p', child) that mounts it, up to the top-level chain, read through as any and as unknown as T casts and resolved through imports (aliased ones included). A chain built inline inside .use(new Elysia({ prefix }).get(…)), a .group('/v2', app => app.get(…)) or .guard() callback, and a module re-exported as export const deckModule = deckRoutes as unknown as Elysia all compose the same way. A Next.js app/api/x/[id]/route.ts exporting GET is GET /api/x/:id. A repository holding a langgraph.json with a non-empty graphs object is a LangGraph Agent Server: its 49 framework routes (POST /runs/wait, POST /threads/{thread_id}/runs/stream, GET /assistants/{assistant_id}, ..., the table @langchain/langgraph-api 1.5.1 registers, less its fo

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