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MCP Server Overview

This page introduces MCP servers and helps you decide if they’re right for your use case.

After reading this page, you will be able to:

Explain what an MCP server is and how tools differ from pipelines
Identify use cases where MCP servers provide business value
Describe how MCP tools expose Redpanda Connect components to AI

What is MCP?

MCP (Model Context Protocol) is an open standard that lets AI agents use tools. Think of it like a universal adapter: instead of building custom integrations for every AI system, you define your tools once using MCP, and any MCP-compatible AI client can discover and use them.

Without MCP, connecting AI to your business systems requires custom API code, authentication handling, and response formatting for each AI platform. With MCP, you describe what a tool does and what inputs it needs, and the protocol handles the rest.

What is Redpanda Connect MCP server?

Redpanda Connect’s MCP server lets you expose data pipelines and automations as AI-consumable tools (MCP tools) with no custom API code. You write tools as YAML configurations, and the MCP server makes them available to AI agents.

This means AI agents can:

Pull information from databases, APIs, message queues, or Redpanda topics
Publish messages, update records, trigger workflows, or call external services
Only access the specific tools you configure and enable

If you already use Redpanda Connect for data integration and processing, adding an MCP server gives AI agents controlled access to your existing systems without extra development effort.

MCP tools are not pipelines

If you already use Redpanda Connect, you might wonder how MCP tools differ from pipelines.

A pipeline is a continuous data flow: data streams from an input, through processors, to an output. The pipeline runs indefinitely, processing many messages over time.

An MCP tool is different. It’s a single component that executes on demand when called by an AI client. The tool starts, runs, and completes for each invocation. There is no persistent state between calls. Think of it like calling a function rather than running a service.

This request/response pattern is what makes MCP tools useful for AI agents: the agent asks a question, the tool runs, and it returns an answer.

Use cases

Category	Example prompts
Operational monitoring	Check partition lag for customer-events topic Show me the top 10 producers by message volume today Get schema registry health status
Data enrichment and analysis	Fetch user profile data and recent orders for customer ID 12345 Get real-time stock prices for symbols in my portfolio topic Analyze sentiment of latest product reviews
Team productivity	Deploy my microservice to the staging environment Generate load test data for the payments service Create a summary dashboard of this week’s incident reports
Business intelligence	What are the trending products in the last 24 hours? Show revenue impact of the latest feature deployment Get customer satisfaction scores from support tickets

How it works

When you start an MCP server, it runs locally on your system and acts as a bridge between AI clients and your data:

A user asks their AI agent something like "What’s the weather in London?"
The AI client connects to the local MCP server and finds the matching tool
The server runs your MCP tools
Your tools fetch data, transform it, and return a response
The AI agent gets the data and can use it to answer the user

What an MCP tool looks like

An MCP tool in Redpanda Connect is a YAML file with two parts: the logic (what the tool does) and the metadata (how AI understands it).

Here’s a minimal example that returns weather data:

http:
  url: "https://wttr.in/${! this.city }?format=j1"
  verb: GET

meta:
  mcp:
    enabled: true
    name: get_weather
    description: "Get current weather for a city"
    properties:
      - name: city
        type: string
        description: "City name"
        required: true

When an AI client asks about weather, it calls this tool with the city name. The tool fetches data from the weather API and returns it.

MCP specification support

MCP servers implement the open MCP protocol for tool exposure. Only the tool concept from the MCP server specification is supported. Features such as MCP resources and prompts are not yet available.

For full details, see the official MCP server specification.