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

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:

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:

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:

from memorysync import MemorySyncClient

client = MemorySyncClient(api_key="ms_live_...")

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:

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!