Zero-Signup Docs MCP: How to Query Technical Documentation Directly Inside Cursor

A developer built a public, zero-signup Documentation MCP Server at docs.memorysync.io/mcp that lets Cursor, Windsurf, and Claude Code autonomously search and read live technical documentation in under 50ms without authentication. The server implements the MCP 2025-06-18 specification and exposes two read-only tools, search_docs for BM25 keyword search and read_doc for clean markdown retrieval, to avoid the hallucinated API problem and reduce context overhead.

If you use Cursor , Windsurf , or Claude Code to build software, you have inevitably encountered the "Hallucinated API" problem : TypeError: Cannot read properties of undefined reading 'call' ImportError: cannot import name 'ChatOpenAI' from 'langchain' The typical workaround is frustrating: you switch tabs, find the official documentation website, copy-paste 3 pages of markdown into the Cursor chat prompt, and watch your context window balloon by 12,000 tokens before you've even written a single line of application logic. There is a significantly better way: The Model Context Protocol MCP . In this guide, we'll walk through how we built and exposed a zero-signup, public Documentation MCP Server at https://docs.memorysync.io/mcp that allows Cursor and Claude Desktop to autonomously search, index, and read live technical documentation in under 50ms with zero authentication required. @Docs Fails in Modern IDEs Cursor has a built-in @Docs crawler, but it suffers from three structural flaws when dealing with rapidly evolving AI libraries: | Limitation | Cursor @Docs Built-in Crawler | Model Context Protocol MCP | |---|---|---| | Freshness | Relies on periodic background web scrapes that go stale | Live Edge Endpoint: Always serves the current production deployment | | Context Overhead | Ingests entire web page HTML/CSS DOM trees | Targeted Markdown Sections: Injects only the exact function signature needed ~150 tokens | | Authentication Barrier | Often gets blocked by Cloudflare turnstiles or paywalls | Open JSON-RPC 2.0 Standard: Zero cookies, zero auth tokens, zero rate-wall hurdles | Instead of forcing developers to download heavy Python or Node.js packages locally just to look up a documentation page, we host an edge JSON-RPC 2.0 server directly at https://docs.memorysync.io/mcp . Here is the exact runtime flow: +-------------------------------------------------------------+ | Cursor Composer | | User types: "How do I store..." | +------------------------------+------------------------------+ | | 1. Auto-calls tool: search docs "store chat turns" v +-------------------------------------------------------------+ | MemorySync Public Docs MCP Server | | https://docs.memorysync.io/mcp | +------------------------------+------------------------------+ | | 2. Returns scored markdown headings & slugs v +-------------------------------------------------------------+ | Cursor Composer | | 2. Auto-calls tool: read doc "/quickstart" | +------------------------------+------------------------------+ | | 3. Returns exact markdown snippet < 200 tokens v +-------------------------------------------------------------+ | Model Writes Bug-Free Code Matching Exact Live API | +-------------------------------------------------------------+ Our public docs server implements the strict MCP 2025-06-18 Specification and exposes three read-only tools: search docs Performs BM25 and keyword search across all indexed documentation sections. { "name": "search docs", "arguments": { "query": "authentication bearer token" } } Returns: Ranked list of URLs, titles, and section headings. read doc Fetches the clean, pure-markdown twin of any documentation page without HTML boilerplate, scripts, or navigational banners. { "name": "read doc", "arguments": { "path": "/guides/cursor" } } Returns: Exact markdown content ready for the LLM to inspect. list doc sections Returns a structural map of the entire documentation hierarchy, including pointers to raw llms.txt and llms-full.txt endpoints. You do not need an account, an API key, or a credit card to use this in your local projects. .cursor/mcp.json In your project's root directory, create a .cursor folder and add an mcp.json file: { "mcpServers": { "memorysync-docs": { "url": "https://docs.memorysync.io/mcp" } } } If you are using Claude Desktop, use npx -y mcp-remote https://docs.memorysync.io/mcp as your stdio-to-SSE bridge . Cmd + , macOS or Ctrl + , Windows/Linux . memorysync-docs .cursorrules Pattern To make Cursor query the documentation autonomously whenever you ask a question so you don't even have to manually type @docs , add this snippet to your root .cursorrules or .cursor/rules/mcp.mdc file: Documentation Query Rule When writing code that integrates with MemorySync or external APIs: 1. NEVER assume or guess method names, SDK signatures, or endpoint parameters. 2. If you are unsure of an API contract, call search docs with the relevant keywords. 3. Inspect the returned slug with read doc before generating code. 4. Always implement code strictly matching the signatures in the returned markdown documentation. Here is what happens when you prompt Cursor Composer: "Show me how to store conversation turns in MemorySync using Python." Instead of guessing from obsolete 2023 training weights, you will see Cursor execute two tool calls in its timeline: memorysync-docs: search docs {"query": "python store turns"} memorysync-docs: read doc {"path": "/sdks/python"} And the generated code uses the exact current SDK: python from memorysync import MemorySyncClient client = MemorySyncClient api key="ms live ..." Correct, verified live SDK method: memory = client.memories.add text="User prefers PostgreSQL over MongoDB for transactional data", metadata={"source": "composer", "importance": 0.9} print f"Memory recorded: {memory.id}" Zero deprecation warnings. Zero hallucinations. Zero manual copy-pasting. We benchmarked a 50-turn agent coding session comparing traditional context-stuffing vs. Docs-over-MCP: | Metric | Raw Copy-Paste Context Stuffing | Docs-over-MCP Dynamic Retrieval | Difference | |---|---|---|---| | Tokens Consumed per Task | 14,200 tokens | 1,850 tokens | -87% Token Reduction | | Prompt Latency | 4.8 seconds | 1.1 seconds | 4.3x Faster Generation | | Hallucinated Methods | 3 occurrences | 0 occurrences | 100% Deterministic Code | By letting the IDE fetch exactly what it needs right when it needs it, your LLM stays in its fast, high-accuracy context sweet spot. If you'd like to test this immediately without manual setup, we published a ready-to-use template: Happy building, and may your AI agents never hallucinate an API signature again