# Docs MCP Server Starter — Live Docs: Claude, Cursor & AI Agents (`joeslade/docs-mcp-server-starter`) Actor Persistent MCP server that gives Claude, Cursor, and any MCP-compatible AI assistant queryable access to technical documentation. Indexes any docs site, exposes search and fetch tools over MCP, caches pages for speed. Ships with templates for Next.js, Tailwind, React, TypeScript, Prisma. - **URL**: https://apify.com/joeslade/docs-mcp-server-starter.md - **Developed by:** [Joe Slade](https://apify.com/joeslade) (community) - **Categories:** MCP servers, AI, Developer tools - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Docs MCP Server Starter Give Claude, Cursor, and any MCP-compatible AI assistant queryable access to up-to-date technical documentation. This Apify Actor runs a persistent MCP server that indexes docs sites, exposes search and fetch tools over the Model Context Protocol, and caches pages for fast follow-up queries. Ships with five ready-to-use templates (Next.js, Tailwind CSS, React, TypeScript, Prisma). Fork it for any docs site by supplying a URL and a CSS content selector — no code changes required. ![Docs MCP Server Starter demo — get_toc, search_docs, and get_page running against a local Standby server](https://joeslade.com/wp-content/uploads/2026/05/demo.gif) ### Who is this for? - **Developers using AI coding assistants** who want answers grounded in the *current* docs, not training-data snapshots that may be months out of date - **Teams with internal documentation** who want their AI tooling to answer questions against their own docs, not just public sources - **MCP builders** who want a working Standby-mode reference implementation to fork ### What it does - Crawls and indexes up to 10 documentation sources on startup - Exposes four MCP tools over JSON-RPC on a persistent HTTP endpoint - Caches fetched pages in an LRU cache (default 50) so repeat queries return instantly - Returns content as clean markdown by default, or raw text on request ### Use cases - **Keep your AI assistant current.** Point it at the Next.js, React, or TypeScript docs so Claude or Cursor answers from today's API surface instead of a months-old training snapshot. - **Query your team's internal docs.** Index a private or internal documentation site (any static HTML) and let your AI tooling answer against your own sources, not just public ones. - **A RAG-free docs layer.** Give an AI agent searchable, fetchable docs without standing up a vector store, embedding pipeline, or re-indexing job — keyword search over live pages. - **Fork it as an MCP reference implementation.** Shipping your own Standby-mode MCP server? Start here — the indexing, caching, and JSON-RPC wiring is already done. ### Input | Field | Type | Default | Notes | | --- | --- | --- | --- | | `sources` | array (1–10) | required | Each source needs a `name` plus either a `template` ID *or* a custom `url` + `contentSelector`. Optional `sitemapUrl`. | | `maxPagesPerSource` | integer (1–500) | 200 | Cap on how many pages per source get indexed at startup. Lower this to speed up boot for large docs sites. | | `cacheMaxPages` | integer (1–200) | 50 | LRU page cache shared across all sources. Raise this if you query the same pages repeatedly. | | `markdownOutput` | boolean | `true` | Convert extracted page HTML to markdown. Set `false` to return raw text. | #### Curated templates `nextjs`, `tailwind`, `react`, `typescript`, `prisma` #### Custom source example ```json { "sources": [ { "name": "Apify SDK", "url": "https://docs.apify.com/sdk/js", "contentSelector": "main article" } ] } ```` ### MCP tools | Tool | Purpose | Required args | | --- | --- | --- | | `list_sources` | List configured sources with page counts | — | | `get_toc` | Return the page index (table of contents) for a source | `source` | | `search_docs` | Case-insensitive keyword search across titles and cached content | `query` (optional: `source`, `maxResults` ≤ 30) | | `get_page` | Fetch full page content; checks LRU cache first | `url` | #### Example tool calls ```json { "jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": { "name": "list_sources", "arguments": {} } } { "jsonrpc": "2.0", "id": 2, "method": "tools/call", "params": { "name": "search_docs", "arguments": { "query": "server actions", "source": "Next.js Docs", "maxResults": 5 } } } { "jsonrpc": "2.0", "id": 3, "method": "tools/call", "params": { "name": "get_page", "arguments": { "url": "https://nextjs.org/docs/app/building-your-application/data-fetching/server-actions" } } } ``` ### How it works 1. **Boot:** `Actor.init()`, validate input, resolve any curated templates 2. **Index:** for each source, fetch the sitemap (or discover from the entry URL), cap at `maxPagesPerSource`, and build a page index (`url` + `title`) 3. **Serve:** start an HTTP server on `ACTOR_STANDBY_PORT` (default `4321`); accept POST requests carrying JSON-RPC MCP messages 4. **Cache:** `get_page` returns cached content when available; on miss it fetches, extracts via the source's `contentSelector`, optionally converts to markdown, and stores in the LRU 5. **Search:** `search_docs` scans page titles in all source indexes and content in the LRU cache only — page bodies are not pre-indexed; warm the cache by calling `get_page` on the pages you want searchable ### Running on Apify This actor runs in **Standby mode** — a persistent HTTP server, not a batch job. Connect an MCP client to the Standby URL exposed by Apify after deploy. The server starts after indexing completes; check the run logs for `MCP server listening.` before sending requests. ### Connect your AI assistant This actor speaks MCP over JSON-RPC at its Standby URL. Grab the exact URL and your access token from the actor's **Standby** tab in the Apify Console after the first run. #### Claude Desktop / Cursor (stdio clients) Most desktop MCP clients speak stdio, so bridge to the remote HTTP endpoint with [`mcp-remote`](https://www.npmjs.com/package/mcp-remote). Add this to your MCP config — `claude_desktop_config.json` for Claude Desktop, or your Cursor MCP settings: ```json { "mcpServers": { "docs": { "command": "npx", "args": [ "-y", "mcp-remote", "https://.apify.actor?token=" ] } } } ``` Replace `` and `` with the values from the Standby tab. (The token can also be sent as an `Authorization: Bearer` header if your client supports custom headers.) #### HTTP / streamable MCP clients Clients that speak MCP over HTTP can point straight at the Standby URL and supply the Apify token per their auth settings. Once connected, the four tools — `list_sources`, `get_toc`, `search_docs`, `get_page` — appear automatically. ### Design choices (v1) - **Keyword search, not vectors.** No embedding costs, no vector store to maintain, predictable behavior. Good fit for docs lookups where terminology is precise. - **Static HTTP fetching only.** Works with any docs site served by a static generator (Hugo, Next.js static export, Docusaurus, WordPress, MkDocs, etc.). Sites that require JS execution or sit behind bot challenges (Cloudflare, login walls) won't index — pick the underlying static source instead. - **Search reads cached bodies; uncached pages match on title.** Warm the cache by calling `get_page` on the pages you want full-text searchable. - **Caps.** Max 10 sources, 500 pages per source, 200 pages in cache. ### FAQ **How do I connect this to Claude Desktop or Cursor?** See [Connect your AI assistant](#connect-your-ai-assistant). Desktop clients bridge to the Standby URL with `mcp-remote`; HTTP-capable clients point at the URL directly. **Does it work with private or internal docs?** Yes — any static-HTML docs site reachable over HTTP. Supply a `url` + `contentSelector` instead of a curated template. Sites behind login walls or bot challenges (Cloudflare, auth gates) won't index; point it at the underlying static source. **How is this different from a vector RAG pipeline?** No embeddings, no vector store. It runs keyword search over page titles and cached page bodies — so there are no embedding costs and the results are predictable and debuggable. That's a good fit when docs terminology is already precise. For fuzzy semantic recall across a huge corpus, a vector approach may suit you better. **Which docs sites are supported out of the box?** Five curated templates: Next.js, Tailwind CSS, React, TypeScript, and Prisma. Any other static docs site works via a custom `url` + `contentSelector`. **Why does search miss some pages?** `search_docs` matches titles across every indexed page, but full-text matching only covers pages already in the cache. Warm the cache by calling `get_page` on the pages you want fully searchable (see [How it works](#how-it-works)). **Does it support JavaScript-rendered docs?** No — v1 uses static HTTP fetching only. JS-rendered or bot-challenged sites won't index; use the underlying static source instead. ### Local development ```bash npm install npm test ## pipeline, cache, indexer, extractor, mcp, searcher, sitemap suites apify run ## local Standby — server listens on ACTOR_STANDBY_PORT or 4321 ``` ### License Apache-2.0 # Actor input Schema ## `sources` (type: `array`): Documentation sources to index on startup. Use a curated template ID (nextjs, tailwind, react, typescript, prisma) or provide a custom url + contentSelector for any docs site. Each source needs a unique name. ## `maxPagesPerSource` (type: `integer`): Cap on how many pages each source can index at startup. Lower this to speed up boot for large docs sites; raise it for fuller coverage. ## `cacheMaxPages` (type: `integer`): How many fetched pages to hold in the LRU cache. Cached pages return instantly and become searchable by content; uncached pages match only on title. ## `markdownOutput` (type: `boolean`): Convert extracted page HTML to clean markdown. Turn off to return raw text instead. ## Actor input object example ```json { "sources": [ { "name": "Next.js Docs", "template": "nextjs" } ], "maxPagesPerSource": 200, "cacheMaxPages": 50, "markdownOutput": true } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sources": [ { "name": "Next.js Docs", "template": "nextjs" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("joeslade/docs-mcp-server-starter").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sources": [{ "name": "Next.js Docs", "template": "nextjs", }] } # Run the Actor and wait for it to finish run = client.actor("joeslade/docs-mcp-server-starter").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sources": [ { "name": "Next.js Docs", "template": "nextjs" } ] }' | apify call joeslade/docs-mcp-server-starter --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=joeslade/docs-mcp-server-starter", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Docs MCP Server Starter — Live Docs: Claude, Cursor & AI Agents", "description": "Persistent MCP server that gives Claude, Cursor, and any MCP-compatible AI assistant queryable access to technical documentation. Indexes any docs site, exposes search and fetch tools over MCP, caches pages for speed. Ships with templates for Next.js, Tailwind, React, TypeScript, Prisma.", "version": "0.2", "x-build-id": "ESJTZib5Z3wC5JgQy" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/joeslade~docs-mcp-server-starter/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-joeslade-docs-mcp-server-starter", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/joeslade~docs-mcp-server-starter/runs": { "post": { "operationId": "runs-sync-joeslade-docs-mcp-server-starter", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/joeslade~docs-mcp-server-starter/run-sync": { "post": { "operationId": "run-sync-joeslade-docs-mcp-server-starter", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "sources": { "title": "Documentation sources", "maxItems": 10, "type": "array", "description": "Documentation sources to index on startup. Use a curated template ID (nextjs, tailwind, react, typescript, prisma) or provide a custom url + contentSelector for any docs site. Each source needs a unique name.", "items": { "type": "object", "required": [ "name" ], "properties": { "name": { "title": "Source name", "type": "string", "description": "Unique label for this source (e.g., \"Next.js Docs\"). Used as the lookup key in MCP tool calls." }, "template": { "title": "Template ID", "type": "string", "description": "Curated template ID: nextjs, tailwind, react, typescript, or prisma. Use this OR url + contentSelector." }, "url": { "title": "Custom URL", "type": "string", "description": "Entry URL for a custom docs site (e.g., https://docs.example.com). Required if no template is set." }, "sitemapUrl": { "title": "Sitemap URL", "type": "string", "description": "Optional explicit sitemap.xml URL. Leave blank to auto-discover from the entry URL." }, "contentSelector": { "title": "Content CSS selector", "type": "string", "description": "CSS selector for the main content area on each page (e.g., \"article\", \"#content\", \"main article\"). Required for custom URLs." } } } }, "maxPagesPerSource": { "title": "Max pages per source", "minimum": 1, "maximum": 500, "type": "integer", "description": "Cap on how many pages each source can index at startup. Lower this to speed up boot for large docs sites; raise it for fuller coverage.", "default": 200 }, "cacheMaxPages": { "title": "Cache max pages", "minimum": 1, "maximum": 200, "type": "integer", "description": "How many fetched pages to hold in the LRU cache. Cached pages return instantly and become searchable by content; uncached pages match only on title.", "default": 50 }, "markdownOutput": { "title": "Markdown output", "type": "boolean", "description": "Convert extracted page HTML to clean markdown. Turn off to return raw text instead.", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```