MCP Connector

Access LLM Documentation Locally or Remotely

AI documentation access server for LLMs. Query local files or remote URLs via MCP.

Works with vscode

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77
Spark score
out of 100
Updated Apr 2025
Version 0.0.12
Models
universal

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Why it matters

Integrate local files or remote URLs as documentation sources for AI agents. This connector allows LLMs to query and retrieve information directly from your documentation.

Outcomes

What it gets done

01

Configure documentation sources via local files or URLs.

02

Retrieve and parse documentation content for AI agent consumption.

03

Enable AI agents to query documentation using provided tools.

04

Integrate with VS Code for enhanced documentation access.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/vb-docs | bash

Capabilities

Tools your agent gets

getDocumentationSources

Displays all available documentation sources that have been configured

getDocumentation

Retrieves and parses documentation from the specified URL or local file

Overview

Docs MCP Server

2024-02-27T17:30:00.000Z

What it does

DocsMCP provides documentation access to Large Language Models (LLMs). It enables LLMs to access and query documentation from specified sources, including local files or remote URLs, using the Model Context Protocol (MCP).

Configuration Example for Cursor:

{
  "mcpServers": {
    "docs-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "docsmcp",
        "'--source=Model Context Protocol (MCP)|https://modelcontextprotocol.io/llms-full.txt'"
      ]
    }
  }
}

Available Tools:

  • getDocumentationSources: Lists all available documentation sources.
  • getDocumentation: Fetches and parses documentation from a given URL or local file path.
Source README

DocsMCP

A Model Context Protocol (MCP) server that provides documentation access to LLMs.

Overview

DocsMCP enables Large Language Models (LLMs) to access and query documentation from specified sources, whether from local files or remote URLs. It uses the Model Context Protocol (MCP) to facilitate communication between the LLM and documentation sources.

Usage

Cursor MCP Configuration

You can also configure DocsMCP in your Cursor project by creating a .cursor/mcp.json file:

{
  "mcpServers": {
    "docs-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "docsmcp",
        "'--source=Model Context Protocol (MCP)|https://modelcontextprotocol.io/llms-full.txt'"
      ]
    }
  }
}

This configuration allows Cursor AI to use the documentation MCP server automatically when you open your project.

Note:

When specifying a source that contains spaces, ensure to wrap the entire string in quotes. For example:
'--source=Model Context Protocol (MCP)|https://modelcontextprotocol.io/llms-full.txt'

VS Code MCP Configuration

You can configure DocsMCP in VS Code by adding a configuration to your .vscode/mcp.json file:

{
  "servers": {
    "documentation-mcp-server": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "docsmcp",
        "--source=Model Context Protocol (MCP)|https://modelcontextprotocol.io/llms-full.txt"
      ]
    }
  }
}

This configuration allows VS Code extensions that support MCP to use the documentation server automatically.

Available Tools

The MCP server provides two main tools:

getDocumentationSources

Lists all available documentation sources that have been configured.

getDocumentation

Fetches and parses documentation from a given URL or local file path.

Parameters:

  • url: The URL or file path to fetch the documentation from

FAQ

Common questions

Discussion

Questions & comments · 0

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