Dataproc Metastore

MCP Server generated by mcp.ag2.ai

python

0

GitHub Stars

python

Language

7 months ago

First Indexed

3 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "ag2-mcp-servers-dataproc-metastore-api": {
      "command": "python",
      "args": [
        "mcp_server/main.py",
        "stdio"
      ],
      "env": {
        "CONFIG": "<CONFIG>",
        "SECURITY": "<SECURITY>",
        "CONFIG_PATH": "<CONFIG_PATH>"
      }
    }
  }
}

An MCP server provides a stable interface to work with a defined API surface, enabling clients to request and manipulate context-rich model data through the MCP protocol. This server focuses on the Metastore API, exposing its MCP-compatible endpoints so you can integrate with your tooling and workflows without implementing the protocol yourself.

How to use

You connect to the MCP server from an MCP client and perform operations through the standard MCP transport. This server runs locally or remotely and exposes a defined set of endpoints that clients can invoke to manage and query context for the Metastore API. Use the client to send requests that create, fetch, update, or delete model-context data as defined by the Metastore MCP surface. The server handles protocol-level details and returns structured responses suitable for your application logic.

How to install

Prerequisites you must have before installing and running the MCP server:

Python 3.9+
pip and uv

Step-by-step installation

git clone <repository-url>
cd mcp_server

pip install -e ".[dev]"

Alternatively, you can use UV to install editable dependencies:

uv pip install --editable ".[dev]"

Run the server

python mcp_server/main.py stdio

Configuring the server

Configure the server using environment variables. The following keys are supported for runtime configuration:

CONFIG_PATH: Path to a JSON configuration file (for example, mcp_server/mcp_config.json).
CONFIG: A JSON string containing the runtime configuration.
SECURITY: Environment variables for security parameters (such as API keys).

Available tools

ruff

Lints and formats the codebase to ensure style and quality across Python files.

mypy

Performs static type checking to catch type errors during development.

pytest

Runs unit tests and provides test coverage information.

pre-commit

Runs a suite of checks (linting, formatting, security) before each commit.

hatch

Used for building and publishing the MCP server package.