docs-mcp-server MCP Server

Name: docs-mcp-server MCP Server
Author: xinlei413

@xinlei413

Repository

About docs-mcp-server MCP Server

No overview available yet

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "MCP-DOC-Server-OpenRouter": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "\\"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is docs-mcp-server?

docs-mcp-server is an MCP server that fetches, indexes, and searches third‑party package documentation from multiple sources (websites, GitHub, npm, PyPI, local files). It processes content into chunks, supports full‑text search via SQLite FTS5, and integrates with OpenRouter API for chat/completions. Vector embeddings are currently disabled, leaving only full‑text search and chat/completions active.

How to use docs-mcp-server?

Run the server via Docker (docker run ... ghcr.io/arabold/docs-mcp-server:latest) or npx (npx -y --package=@arabold/docs-mcp-server docs-server), configured with your MCP client (e.g., Claude, Cline, Roo). An OpenAI API key is required. Use MCP tools such as scrape_docs, search_docs, and list_libraries, or use the included CLI (docs-cli).

Key features of docs-mcp-server

Versatile scraping: websites, GitHub, npm, PyPI, local files
Asynchronous job handling for scraping and indexing
Full‑text search with SQLite FTS5
OpenRouter API integration for chat/completions
Multiple embedding providers supported (currently disabled)
Simple deployment via Docker or npx

Use cases of docs-mcp-server

Index documentation of a library and search it via AI assistant
Fetch docs from multiple sources (web, GitHub, package registries)
Manage different versions of library documentation
Use chat/completions to answer questions about scraped content

FAQ from docs-mcp-server

Are embeddings/vector search still available?

No. The current version has removed all embedding functionality. Only full‑text search and chat/completions are provided.

How do I run the server?

Use Docker with the image ghcr.io/arabold/docs-mcp-server:latest and set the required OPENAI_API_KEY environment variable. Alternatively, run via npx: npx -y --package=@arabold/docs-mcp-server docs-server.

Where is the indexed data stored?

Data is stored in an SQLite database inside the container’s /data directory. Persist it by mounting a Docker volume (e.g., -v docs-mcp-data:/data). The npx mode uses a default location in your home directory.

What dependencies are required?

For Docker: only Docker itself. For npx: Node.js must be installed. An API key (e.g., OpenAI) is required for chat/completions and was needed for embeddings when enabled.

Can I use the OpenRouter API?

Yes. Set OPENAI_API_KEY to your OpenRouter API key and optionally OPENAI_API_BASE to https://openrouter.ai/api/v1. Many models are supported (GPT‑4.1, Claude, Gemini, etc.).

Comments

More Memory & Knowledge MCP servers

MemoryMesh

CheMiguel23

A knowledge graph server that uses the Model Context Protocol (MCP) to provide structured memory persistence for AI models.

348

🧠 Ultimate MCP Server

Dicklesworthstone

Comprehensive MCP server exposing dozens of capabilities to AI agents: multi-provider LLM delegation, browser automation, document processing, vector ops, and cognitive memory systems

154