Apache Beam MCP Server

@souravch

Basic information

About Apache Beam MCP Server

MCP server to manage apache beam workflows with different runners

Apache Beam MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "beam-mcp-server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "beam-mcp-venv"
      ]
    }
  }
}

Apache Beam MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Apache Beam MCP Server?

The Apache Beam MCP Server is a Model Context Protocol server for managing Apache Beam data pipelines across multiple runners including Flink, Spark, Dataflow, and Direct. It provides a standardized API for data engineers, AI/LLM developers, and DevOps teams to create, monitor, and control pipelines.

How to use Apache Beam MCP Server?

Clone the repository, create a Python virtual environment (3.9+), install dependencies from requirements.txt, then start the server with python main.py --debug --port 8888. For runner-specific configurations, set CONFIG_PATH environment variable. Pipelines are submitted via HTTP POST to /api/v1/jobs with job parameters in JSON. Use the Python client library or curl for interaction.

Key features of Apache Beam MCP Server

Multi-runner support: Flink, Spark, Dataflow, and Direct
MCP compliant for AI/LLM integration
Pipeline management: create, monitor, and control jobs
Easy to extend with new runners or custom features
Production-ready with Docker, Kubernetes, and monitoring

Use cases of Apache Beam MCP Server

Manage Apache Beam pipelines with a consistent API across different runners
Enable AI-controlled data pipelines through the MCP standard
Simplify pipeline operations and monitoring for DevOps teams
Deploy and scale pipelines in Docker or Kubernetes environments

Frequently asked questions

Which Apache Beam runners are supported?

The server supports Flink, Spark, Dataflow, and Direct runners.

How can I deploy the server in a containerized environment?

Pre-built Docker images are available on GitHub Container Registry. Kubernetes deployment is supported via `kubectl apply` or Helm chart, with detailed instructions in the deployment guide.

What monitoring and observability features are included?

Prometheus metrics are exposed at `/metrics`, pre-configured Grafana dashboards are available, structured JSON logging is used, and a health check endpoint is provided at `/health`.

What are the runtime dependencies?

The server requires Python 3.9+ and Apache Beam 2.50.0. It can run with the Direct runner (no external dependencies) or with Flink, Spark, or Dataflow runners if those are installed.

What API endpoints are available for pipeline management?

The server implements MCP standard endpoints (`/tools`, `/resources`, `/contexts`) plus pipeline-specific endpoints at `/api/v1/jobs` for submitting, monitoring, and managing jobs.

Comments

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