mcp-knowledge-server 0.1.8

MCP server exposing internal knowledge retrieval tools.

MCP Knowledge Tools

This repository implements a Model Context Protocol (MCP) server that exposes three HTTP-backed retrieval tools for internal knowledge bases.

Available tools

Tool name	Description	Dataset ID
query_ux_knowledge	Retrieves UX guidelines, templates, and examples.	cab02597-6315-456c-92d3-19a65e3e7efd
query_lean_knowledge	Retrieves Lean / Continuous Improvement documentation.	67659dbe-4387-4122-8eb9-1d2005bea6a2
query_automation_step	Retrieves automation process steps and related materials.	b68de37f-a9f7-41fc-948f-eb89ca145770

Each tool sends a POST request to the Dify dataset retrieval API:

POST https://api.dify.ai/v1/datasets/{data_id}/retrieve
Authorization: Bearer <dataset token>
Content-Type: application/json
{"query": "<your question>"}

Configuration

Set the dataset token through the DIFY_DATASET_TOKEN environment variable. A fallback token (dataset-gCRaKZgnKtvqLdeuoCFjKiME) is bundled for quick testing, but production deployments should override it.

export DIFY_DATASET_TOKEN="dataset-..."

Installation

The project is published as a standard Python package and can be installed with your preferred Python packaging tool.

Using uv

uv offers fast Python environment management and package installation.

uv venv
source .venv/bin/activate
uv pip install .

Using pip

The project is published as a standard Python package.

python -m venv .venv
source .venv/bin/activate
pip install .

Running the MCP server

The pyproject.toml file defines a console script entry point. After installation you can launch the server via:

mcp-knowledge-server

Alternatively you can run the module directly:

uv run mcp-knowledge-server

The server uses FastMCP from the Model Context Protocol reference implementation, so it can be registered with compatible MCP clients.