Skip to main content
Glama
dymk

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

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • RagDocs MCP Server

    heltonteixeira
    A
    license
    Not graded
    quality
    Not graded
    maintenance
    Provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, allowing users to add, search, list, and delete documentation with metadata support.
    16 npm
    16
    -

  • RAG Database MCP Server

    Human-center
    F
    license
    Not graded
    quality
    D
    maintenance
    Enables AI assistants to search and query PDF documents through a local RAG system with vector embeddings. Provides semantic document search capabilities while keeping all data stored locally without external dependencies.
    -

  • PDF RAG MCP Server

    wesleygriffin
    A
    license
    Not graded
    quality
    D
    maintenance
    Enables intelligent search and question-answering over PDF documents using semantic similarity and keyword search. Supports OCR for scanned PDFs, persistent vector storage with ChromaDB, and maintains source tracking with page numbers.
    6
    MIT

  • RooCode-RAG-Lookup

    Mazchoo
    F
    license
    Not graded
    quality
    D
    maintenance
    Enables semantic search across documents and code repositories using RAG (Retrieval-Augmented Generation) with vector embeddings. Automatically indexes PDF documents and performs relevance-scored lookups through ChromaDB and sentence transformers.
    -