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AskDocs MCP Server

AskDocs MCP Server

by dymk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

AskDocs MCP Server

A Model Context Protocol (MCP) server that provides RAG-powered semantic search over technical documentation PDFs using Ollama.

Features

  • Semantic search with natural language queries

  • Multiple PDF documents with page citations

  • Docker support with persistent caching

  • TOML-based configuration

Related MCP server: RAG Database MCP Server

Quick Start

1. Create askdocs-mcp.toml in your project's docs directory:

[[doc]]
name = "my_manual"
description = "My Product Manual"
path = "pdf/manual.pdf"

2. Run with Docker:

docker run -it --rm --network=host -v ./docs:/docs askdocs-mcp:latest

askdocs-mcp expects an Ollama server to be running on http://localhost:11434.

3. Directory structure:

docs/
├── askdocs-mcp.toml    # Configuration
├── .askdocs-cache/     # Vector store (auto-created)
└── pdf/
    └── manual.pdf

Add **/.askdocs-cache/** to your .gitignore file.

Configuration

# Optional: Configure models
embedding_model = "snowflake-arctic-embed:latest"
llm_model = "qwen3:14b"

[[doc]]
name = "unique_identifier"
description = "Human description"
path = "pdf/document.pdf"

Using the MCP Server:

Cursor (~/.cursor/mcp.json or <project-root>/.cursor/mcp.json)

{
    "mcpServers": {
        "askdocs-mcp": {
            "command": "docker",
            "args": [
                "run", "-i", "--rm",
                "--network=host",
                "--volume=${workspaceFolder}/docs:/docs",
                "ghcr.io/dymk/askdocs-mcp:latest"
            ]
        }
    }
}

Codex (~/.codex/config.toml)

[mcp_servers.askdocs-mcp]
command = "docker"
args = [
    "run", "-i", "--rm",
    "--network=host",
    "--volume=/your/workspace/folder/docs:/docs",
    "ghcr.io/dymk/askdocs-mcp:latest"
]

Environment variable:

  • ASKDOCS_OLLAMA_URL: Ollama server URL (default: http://localhost:11434)

Available Tools

list_docs()

List all documentation sources.

ask_docs(source_name: str, query: str)

Search documentation with natural language.

get_doc_page(source_name: str, page_start: int, page_end: int = None)

Retrieve full text from specific pages.

Requirements

Ollama must be running with the required models:

ollama pull snowflake-arctic-embed:latest
ollama pull qwen3:14b

Building

# Docker
docker build -t askdocs-mcp:latest .

# Local
uv sync
uv run askdocs-mcp --docs-dir /path/to/docs

License

MIT

Tool Schema Changelog

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