Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Repository intelligence, code graph, history, papertrail, and cross-agent memory for coding agents.
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": {
"rag-rat": {
"command": "npx",
"args": [
"-y",
"@rag-rat/bin",
"mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add rag-rat -- npx -y @rag-rat/bin mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"rag-rat": {
"command": "npx",
"args": [
"-y",
"@rag-rat/bin",
"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": {
"rag-rat": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@rag-rat/bin",
"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 cq27-dev, 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 (0.24.1) llegó al registro hace 22 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.
What a repository knows about itself. rag-rat is a local repo-intelligence index and MCP server for coding agents. It keeps source files read-only, writes only its own SQLite database, and answers with provenance on every result — current source, the code graph, git/GitHub history, and durable, source-anchored repo memories that persist across sessions and agents.
Explore the live VS Code Lens demo — no installation required. It surfaces clone classes, repo memories, and issue/decision context alongside the code; press Ctrl+Alt+R to reveal clone overlays.
Every coding harness already has grep and file reads. rag-rat adds the layer they do not provide: source-anchored *rationale*. It connects the code an agent is about to touch to its callers, callees, tests, git/GitHub history, prior decisions, invariants, risks, and duplicate-code signals — and labels every result with confidence and coverage, so an agent can judge it instead of trusting it.
sequenceDiagram
participant Repo as Repository participant Engine as rag-rat engine participant Agent as Coding agent
Repo->>Engine: Source · git/GitHub · repo memories Engine->>Engine: Index → graph → (opt) SCIP oracle → reconcile Agent->>Engine: where / why / who-calls / impact? Engine-->>Agent: source + call paths + papertrail + memories (with provenance) Agent->>Engine: record a finding Engine->>Repo: persist a source-anchored repo memory
Invariant, Decision, Risk, …) that survive refactors and surface automatically during future queries — the signal grep can't give you. They are *not* assistant memory: they are versioned, local, source-anchored facts about this repository that any future agent retrieves with evidence.Compiler confidence and ranks the load-bearing symbols.For Claude Code, Codex, and opencode, install the plugin. It registers the MCP server, adds the hooks, and fetches a version-matched rag-rat binary on first run, which the MCP server exposes as ~/.local/bin/rag-rat when it starts — see Running the CLI (the Claude Code and Codex bundles also add the skills; on opencode add them with npx @rag-rat/skills):
# Claude Code
claude plugin marketplace add cq27-dev/rag-rat claude plugin install rag-rat@rag-rat
codex plugin marketplace add cq27-dev/rag-rat codex plugin add rag-rat@rag-rat
opencode plugin @rag-rat/plugin-opencode
After installing, approve the plugin so its tools and hooks run (opencode loads plugins without an approval step — nothing to do there):
~/.claude/settings.json with "permissions": { "allow": ["mcp__plugin_rag-rat_rag-rat__*"] } (plugin tools are named mcp__plugin_rag-rat_rag-rat__ ; a server added by hand with claude mcp add uses mcp__rag-rat__* instead).codex session started *inside the repo* (the plugin ships grep-augmentation, clone-check, and session-digest hooks that run outside the sandbox). Choose "Trust all and continue" to enable them. For unattended commands such as codex review, also allow the plugin's MCP tools in ~/.codex/config.toml so the run cannot stall on a per-tool approval prompt: [plugins."rag-rat@rag-rat".mcp_servers.rag-rat]
default_tools_approval_mode = "approve"
This trusts every current and future MCP tool exposed by the installed rag-rat plugin. Only enable it when you trust the plugin's source and installation origin, then restart Codex.
Then open the repository and ask:
Set up rag-rat in this repo.
The init-rag-rat skill scans the repo, explains the material choices, previews rag-rat.toml, writes and indexes only after confirmation, and offers to set up the git hooks that keep the index fresh. The MCP server starts dormant in an unconfigured repo; when setup finishes, reconnect it so it restarts fully active against the new index — in Claude Code run /mcp and reconnect rag-rat; in Codex and opencode, start a new session.
Then put it to work — the loop rag-rat is built for is in Try it.
Manual installation and other agents
Use this path for the standalone CLI, agents without plugin support, or building from source.
The prebuilt package needs no Rust toolchain and supports Apple Silicon macOS, glibc ≥2.38 Linux (x86-64 and arm64), Windows x64, and Android/Termux arm64:
npm install -g @rag-rat/bin
npx @rag-rat/bin --help
@rag-rat/bin fetches the full binary from the matching GitHub release. FastEmbed's ONNX Runtime is statically linked.
To build from source instead:
cargo install rag-rat
cargo install --path crates/rag-rat-cli --bin rag-rat
The default source build needs glibc ≥2.38 and is unavailable for Intel macOS and musl/Alpine. On those platforms, including Ubuntu 22.04, use the pure-Rust embedder:
cargo install rag-rat --no-default-features --features model2vec
--no-default-features alone produces a smaller hash-only build without real embeddings. SQLite is bundled; see Platform support for toolchain details.
cd /path/to/your/repo
rag-rat init
init is an interactive terminal wizard: it scans the repo, guides language and embedding choices, writes rag-rat.toml, and builds the initial index. For a non-interactive setup use rag-rat init --yes, which takes the defaults and installs the git maintenance hooks (add --no-hooks to skip them; --dry-run previews the config without writing). --yes never replaces an existing rag-rat.toml unless you add --force — rerun the wizard to reconfigure instead. Configuration reference: docs/config.md.
Install the skills for Claude Code, Codex, Cursor, and 70+ other detected agents:
npx @rag-rat/skills
That installs using-rag-rat, dream-review, init-rag-rat, and configure-rag-rat-dream. See skills/README.md for per-agent flags and update, list, and remove.
The MCP server uses STDIO: the client launches rag-rat mcp from the repository so it discovers the correct rag-rat.toml and repository scope in the consolidated machine-global store.
claude mcp add --scope project rag-rat -- rag-rat mcp
codex mcp add rag-rat -- rag-rat mcp
Or add the equivalent project configuration:
{
"mcpServers": { "rag-rat": { "command": "rag-rat", "args": ["mcp"] } } }
rag-rat init prints the registration command but does not register the server itself. Pass rag-rat mcp --json if the client must parse JSON; tool text defaults to TOON. Full tool schemas: docs/mcp-tools.md.
Claude Code tool permissions
Claude Code asks once before each rag-rat MCP tool first runs. Choose "Yes, don't ask again," or allow the tool namespace in ~/.claude/settings.json:
{ "permissions": { "allow": ["mcp__rag-rat__*"] } }
Do not pin a global server to one repository's config. A user-scoped server with
--config /some/repo/rag-rat.tomlserves that repository everywhere. Register MCP per project and
let the process discover the config from its working directory.
Once the repo is indexed, the code graph, symbols, git history, semantic search, and clone detection are ready — these answer on the first query. Repo memories start empty: they accrue as agents record findings with memory_create and then surface automatically in later answers. (Tracker issue/PR rationale needs a rag-rat papertrail sync.)
Ask your MCP client:
impact_surface on the function I'm about to edit — its callers, callees, tests, and recent commits."semantic_search over source and docs.important_symbols.find_clones (and the write-time hook warns as you write it).parse_config: reload must not allocate after the scheduler starts." — memory_create writes your first repo memory; it then rides along in future impact_surface / symbol_lookup results.Or from the CLI:
rag-rat query "where is config reload handled?"
rag-rat important-symbols --limit 20 rag-rat brief --mode spine rag-rat clusters --limit 10 rag-rat tools impact-surface --symbol parse_config
The point isn't the tool catalog — it's the loop an agent runs *around* an edit, so it changes code with the callers, tests, rationale, and prior art in front of it instead of guessing:
Solo se muestra el principio; lee el resto en el repo