Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Code graph for coding agents: symbol search, callers, blast radius and cross-service impact.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"cgraph": {
"command": "npx",
"args": [
"-y",
"cgraph-mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add cgraph -- npx -y cgraph-mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"cgraph": {
"command": "npx",
"args": [
"-y",
"cgraph-mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"cgraph": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"cgraph-mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub Nxtsoft, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.9.7) llegó al registro hace 16 días.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Context7, Codebase Memory, Exa
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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.*
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
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 radius | graph_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 grep | Centrality-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 glance | Centrality ranking surfaces the load-bearing files; Leiden/Louvain community detection clusters the repo into modules automatically. |
| ⚡ Deterministic & fast | A full 10k-node graph builds in ~2 s; no LLM in the extraction path — same input, same graph, every time. |
| 🤖 Built for coding agents | A 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.
graph.json, graph.html, graph.svg, obsidian.md, cypher.txt, modules.mmd, modules.svg, design.mmd, design.md).query / path / explain / impact / context in ~10 ms.The engine owns deterministic extraction, fragment validation, cache state, and local graph mutation; hosts own model choice and semantic enrichment.
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
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.
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 fromgraph.html — browser-readable interactive graph viewgraph.svg — static graph visualizationobsidian.md — markdown export for Obsidian-style navigationcypher.txt — Neo4j Cypher statementsmodules.mmd — module dependency diagram as Mermaidmodules.svg — module dependency diagram as a layered static imagedesign.mmd — entry points and their top call flows as a Mermaid flowchart TDdesign.md — the same as Markdown: entry-point table, nested flows, layers by call distanceThe 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:
| graph | time-to-settle before | after | per-frame repaint |
|---|---|---|---|
| 10,000 nodes | 35.2 s (main thread pinned) | ~0 ms (static layout) | 20–34× cheaper (zoom/pan) |
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.
| Method | Recall@20 | MRR | BCY@8k |
|---|---|---|---|
| CGraph bin-v0.9.1 (label + code-text search) | 0.6362 | 0.2787 | 0.4319 |
| CGraph bin-v0.8.3 (label-only text search) | 0.5726 | 0.2356 | 0.3790 |
| RepoMap (published) | 0.6333 | 0.2158 | 0.3788 |
| RepoMap (rerun on this harness) | 0.6311 | 0.2156 | 0.3846 |
| Qwen3-Embedding-8B (published) | 0.7029 | 0.2336 | 0.3732 |
| Qwen3-Embedding-4B (published) | 0.6306 | 0.2379 | 0.3409 |
| BM25 (published) | 0.4452 | 0.1520 | 0.2051 |
How bin-v0.9.1 compares:
Per-task tables, pins, the noise floor and caveats are in bench/arb/RESULTS.md. Reproduce with bench/arb/run.sh.
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.
Seven languages emit a field node per declared member, with a defines edge from the owner:
| Language | Owners with members | Field properties |
|---|---|---|
| C, C++ | class, struct only — not enum or union | none |
| TypeScript, TSX | interface, object type alias, class (constructor parameter properties included), enum | type_text, optional, readonly |
| Go | struct, including multi-name and embedded fields | type_text |
| Rust | named and tuple struct, union, enum variant, trait associated type and constant | type_text |
| Python | class-body assignment, including chained and tuple targets | type_text when annotated |
| Java | class, record, enum | type_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.
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
Solo se muestra el principio; lee el resto en el repo