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mcp-server-python

MCP.Pizza Chef: inkeep

mcp-server-python is an MCP server that integrates with Inkeep to provide retrieval-augmented generation (RAG) capabilities using your documentation and product content. It requires an Inkeep account and uses the uv Python project manager for setup. This server enables secure API key-based access to structured knowledge, facilitating real-time context feeding into LLMs for enhanced AI workflows.

Use This MCP server To

Serve product documentation as structured context for LLMs Enable retrieval-augmented generation using company knowledge bases Provide API access to curated product content for AI agents Integrate with MCP clients to supply real-time document context Manage and update knowledge sources via Inkeep platform Authenticate API requests with generated API keys Host Python-based MCP server for custom AI workflows

README

mcp-server-python

Inkeep MCP Server powered by your docs and product content.

Dependencies

  • An account on Inkeep to manage and provide the RAG
  • uv Python project manager

Local Setup

git clone https://github.com/inkeep/mcp-server-python.git
cd mcp-server-python
uv venv
uv pip install -r pyproject.toml

Note the full path of the project, referred to as <YOUR_INKEEP_MCP_SERVER_ABSOLUTE_PATH> in a later step.

Get an API key

  1. Log in to the Inkeep Dashboard
  2. Navigate to the Projects section and select your project
  3. Open the Integrations tab
  4. Click Create Integration and choose API from the options
  5. Enter a Name for your new API integration.
  6. Click on Create
  7. A generated API key will appear that you can use to authenticate API requests.

We'll refer to this API key as the <YOUR_INKEEP_API_KEY> in later steps.

Add to your MCP client

Follow the steps in this guide to setup Claude Dekstop.

In your claude_desktop_config.json file, add the following entry to mcpServers.

{
    "mcpServers": {
        "inkeep-mcp-server": {
            "command": "uv",
            "args": [
                "--directory",
                "<YOUR_INKEEP_MCP_SERVER_ABSOLUTE_PATH>",
                "run",
                "-m",
                "inkeep_mcp_server"
            ],
            "env": {
                "INKEEP_API_BASE_URL": "https://api.inkeep.com/v1",
                "INKEEP_API_KEY": "<YOUR_INKEEP_API_KEY>",
                "INKEEP_API_MODEL": "inkeep-rag",
                "INKEEP_MCP_TOOL_NAME": "search-product-content",
                "INKEEP_MCP_TOOL_DESCRIPTION": "Retrieves product documentation about Inkeep. The query should be framed as a conversational question about Inkeep."
            }
        },
    }
}

You may need to put the full path to the uv executable in the command field. You can get this by running which uv on MacOS/Linux or where uv on Windows.

mcp-server-python FAQ

How do I set up mcp-server-python locally?
Clone the repo, use uv to create a virtual environment, and install dependencies from pyproject.toml.
What is required to use mcp-server-python?
An Inkeep account to manage RAG content and an API key for authentication.
How do I obtain an API key for mcp-server-python?
Log into the Inkeep Dashboard, create an API integration under your project, and copy the generated key.
Can mcp-server-python work with any LLM provider?
Yes, it is provider-agnostic and works with OpenAI, Claude, Gemini, and others.
What is the role of the uv tool in mcp-server-python?
uv manages the Python project environment and dependencies for easy setup.
How does mcp-server-python handle document updates?
Updates are managed through the Inkeep platform, which syncs content served by the MCP server.
Is mcp-server-python secure for production use?
Yes, it uses API key authentication to secure access to your knowledge base.
Can I customize the content served by mcp-server-python?
Yes, by managing your documents and product content within Inkeep.