Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Google Search Console for AI agents: search analytics, sitemaps, URL inspection.
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.
GSC_CREDENTIALS_PATHNecesitas
¿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": {
"gsc": {
"command": "npx",
"args": [
"-y",
"@conorbronsdon/gsc-mcp"
],
"env": {
"GSC_CREDENTIALS_PATH": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add gsc --env GSC_CREDENTIALS_PATH=your-value -- npx -y @conorbronsdon/gsc-mcpCompleta los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"gsc": {
"command": "npx",
"args": [
"-y",
"@conorbronsdon/gsc-mcp"
],
"env": {
"GSC_CREDENTIALS_PATH": "your-value"
}
}
}
}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": {
"gsc": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@conorbronsdon/gsc-mcp"
],
"env": {
"GSC_CREDENTIALS_PATH": "your-value"
}
}
}
}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.
GSC_CREDENTIALS_PATH envEsta 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 7 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub conorbronsdon, 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 (1.1.0) llegó al registro hace 3 meses.
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.
Maintenance mode. This project is stable and receives security fixes. New features aren't planned, but issues and pull requests are still welcome.
Google Search Console for AI agents: search performance, striking-distance keywords, sitemaps, and URL inspection. SEO automation you can hand to an assistant.
An MCP server for Google Search Console. It gives an AI assistant the data and the levers behind organic search: how queries and pages perform, which keywords are one nudge away from page one, the state of your sitemaps, and whether a specific URL is indexed — plus the ability to submit or remove sitemaps.
Why this exists. Search Console is where SEO actually happens, but its UI is click-heavy and its data is hard to act on at a glance. The single most useful view — queries ranking in *striking distance* of page one — is not even a built-in report; you have to filter and eyeball it. This server puts that view, and the rest of the funnel, in front of an agent so the boring parts of SEO (find the near-miss keywords, check index status, keep sitemaps healthy) can be automated.
It was built to run SEO for two sites — a podcast and a personal site — but nothing here is specific to them. Point it at any property you have verified.
| Tool | Access | What it returns | API |
|---|---|---|---|
gsc_list_sites | read | Verified properties + permission level | GET webmasters/v3/sites |
gsc_search_analytics | read | Clicks/impressions/CTR/position by query, page, country, device, or date | POST .../searchAnalytics/query |
gsc_striking_distance | read | Queries at avg position 8–25 with enough impressions — the optimization list | computed from search analytics |
gsc_list_sitemaps | read | Submitted sitemaps with status, last download, warning/error counts | GET .../sitemaps |
gsc_submit_sitemap | write | Submits/pings a sitemap to Google | PUT .../sitemaps/{feedpath} |
gsc_delete_sitemap | write, destructive | Deregisters a sitemap from Search Console | DELETE .../sitemaps/{feedpath} |
gsc_inspect_url | read | Index status, last crawl, canonical, mobile/rich-results verdicts | POST searchconsole/v1/urlInspection/index:inspect |
Every tool carries MCP read/write annotations, so a client can tell the two write tools apart from the five read-only ones before calling them. Read tools are readOnlyHint: true; gsc_submit_sitemap is a non-destructive write; gsc_delete_sitemap is destructiveHint: true.
List tools default row_limit / limit low (25) to keep responses small — agents pay tokens per response.
This server uses an OAuth installed-app credential (a saved refresh token), not a service account or an API key. It reads the credential from:
~/.config/gws/searchconsole_credentials.json
The file is google-auth's Credentials.to_json() shape: it contains client_id, client_secret, refresh_token, and scopes. The server refreshes short-lived access tokens against Google's token endpoint directly with fetch — no heavy googleapis dependency.
To create the file yourself if you do not already have it:
client_secret.json.~/.config/gws/searchconsole_credentials.json. Any standard google-auth-oauthlib installed-app snippet works — about ten lines, and it opens a browser for a single consent click.The path is shared on purpose: if you already hold a Search Console credential in that location for other tooling, this server reuses it rather than asking you to mint a second one.
gsc_inspect_url, work with https://www.googleapis.com/auth/webmasters.readonly.gsc_submit_sitemap, gsc_delete_sitemap) need the full https://www.googleapis.com/auth/webmasters scope.If your saved credential only has the read-only scope, the read tools work and the write tools return a clear "re-mint with the full scope" error rather than a cryptic 403. Re-running the mint with the full scope upgrades the same file in place; read tools keep working throughout.
Set GSC_CREDENTIALS_PATH to point at a different credential file if you do not use the default location.
The server boots and answers tools/list even when no credential is present, so MCP inspectors can introspect it. Tool *calls* then return a setup pointer (to stderr at startup, and as the tool result). stdout is reserved for the MCP transport and stays clean.
Add your site at search.google.com/search-console. A Domain property (verified with a DNS TXT record) covers http/https and all subdomains and is the recommended choice. Note the exact property string — gsc_list_sites will show it as sc-domain:example.com (Domain) or https://example.com/ (URL-prefix).
Create ~/.config/gws/searchconsole_credentials.json as described under Authentication, requesting the full webmasters scope if you want the two sitemap write tools.
Add to your .mcp.json:
{
"mcpServers": { "gsc": { "command": "npx", "args": ["-y", "@conorbronsdon/gsc-mcp"] } } }
Add to your claude_desktop_config.json:
{
"mcpServers": { "gsc": { "command": "npx", "args": ["-y", "@conorbronsdon/gsc-mcp"] } } }
No env block is needed when the credential is at the default path. Set GSC_CREDENTIALS_PATH if it lives elsewhere.
Ask your assistant: "List my Search Console properties," then "Show me striking-distance keywords for it over the last 28 days."
Read these so you know what the numbers mean and what this can and cannot do.
JobPosting and BroadcastEvent pages, not normal content, and using it for normal pages violates its terms. So there is deliberately no "ask Google to index this page" tool here; for normal pages, requesting indexing is a manual click in the Search Console UI. gsc_submit_sitemap is the supported, API-backed way to nudge crawling.row_limit caps at 1000 here (the API allows 25,000 with pagination); for very large pulls, page with start_date/dimension filters rather than asking for everything at once.row_limit and inspection volume modest.git clone https://github.com/conorbronsdon/gsc-mcp.git
cd gsc-mcp npm install npm run build npm test
Run locally:
npm start
Tests mock fetch and make no network calls.
The package also exports its tools for embedding, so another MCP server can mount the Search Console tools next to its own instead of running a second process. Importing the package starts nothing and reads no credential.
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { DEFAULT_CREDENTIAL_PATH, GSCClient, GoogleAuth, registerGscTools } from "@conorbronsdon/gsc-mcp";
const server = new McpServer({ name: "my-server", version: "1.0.0" }); const auth = GoogleAuth.tryLoad(DEFAULT_CREDENTIAL_PATH); registerGscTools(server, auth ? new GSCClient(auth) : null);
The mounted tools have the same names, schemas, annotations and results as the standalone server. With a null client they still appear in tools/list and return a setup error when called; pass { credentialPath } as the third argument to name a non-default path in that error.
Issues and pull requests are welcome. If a Search Console endpoint is worth wrapping as a tool, open an issue describing what it should return and the endpoint it maps to. Keep the contract honest: read tools stay read-only, write tools carry the right annotations, and responses stay compact.
Search Console is one credential family in a wider Google data stack. The siblings, each with its own auth:
| Data | Server |
|---|---|
| Google Workspace (Gmail, Calendar, Drive, Sheets, Docs, Tasks) | gws-mcp-server — same curated approach: narrow surface, side effects declared on every tool |
| YouTube Analytics | yt-analytics-mcp — owner-side watch time, retention, traffic sources, and playlist metrics; read-only |
| Google Analytics 4 | googleanalytics/google-analytics-mcp — Google's own, read-only |
| BigQuery | googleapis/mcp-toolbox — Google's own |
Nothing here shares a token: this server uses a webmasters OAuth credential, Workspace uses gws auth login, YouTube Analytics uses a yt-analytics.readonly OAuth credential, GA4 uses Application Default Credentials scoped analytics.readonly.
Built and maintained by Conor Bronsdon. I host the Chain of Thought podcast, which covers AI infrastructure, developer tools, and how practitioners actually use this stuff. I built this to pull SEO work into the agent workflows that run the show and my site.
Companion tools:
Solo se muestra el principio; lee el resto en el repo