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Mcp Agent

Build effective agents using Model Context Protocol and simple workflow patterns

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Free

API key required

Works With

Claude CodeCursorWindsurfVS CodeDeveloper tool

About

Build effective agents with Model Context Protocol using simple, composable patterns.

Examples

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Building Effective Agents

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MCP

Overview

`mcp-agent` is a simple, composable framework to build effective agents using Model Context Protocol.

[!Note] mcp-agent's vision is that _MCP is all you need to build agents, and that simple patterns are more robust than complex architectures for shipping high-quality agents_.

mcp-agent gives you the following:

  1. 1.Full MCP support: It _fully_ implements MCP, and handles the pesky business of managing the lifecycle of MCP server connections so you don't have to.
  2. 2.Effective agent patterns: It implements every pattern described in Anthropic's Building Effective Agents in a _composable_ way, allowing you to chain these patterns together.
  3. 3.Durable agents: It works for simple agents and scales to sophisticated workflows built on Temporal so you can pause, resume, and recover without any API changes to your agent.

Altogether, this is the simplest and easiest way to build robust agent applications.

We welcome all kinds of contributions, feedback and your help in improving this project.

Minimal example

python
import asyncio

from mcp_agent.app import MCPApp
from mcp_agent.agents.agent import Agent
from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM

app = MCPApp(name="hello_world")

async def main():
    async with app.run():
        agent = Agent(
            name="finder",
            instruction="Use filesystem and fetch to answer questions.",
            server_names=["filesystem", "fetch"],
        )
        async with agent:
            llm = await agent.attach_llm(OpenAIAugmentedLLM)
            answer = await llm.generate_str("Summarize README.md in two sentences.")
            print(answer)


if __name__ == "__main__":
    asyncio.run(main())

# Add your LLM API key to `mcp_agent.secrets.yaml` or set it in env.
# The [Getting Started guide](https://docs.mcp-agent.com/get-started/overview) walks through configuration and secrets in detail.

At a glance

Don't lose this

Three weeks from now, you'll want Mcp Agent again. Will you remember where to find it?

Save it to your library and the next time you need Mcp Agent, it’s one tap away — from any AI app you use. Group it into a bench with the rest of the team for that kind of task and you can pull the whole stack at once.

⚡ Pro tip for geeks: add a-gnt 🤵🏻‍♂️ as a custom connector in Claude or a custom GPT in ChatGPT — one click and your library is right there in the chat. Or, if you’re in an editor, install the a-gnt MCP server and say “use my [bench name]” in Claude Code, Cursor, VS Code, or Windsurf.

🤵🏻‍♂️

a-gnt's Take

Our honest review

This plugs directly into your AI and gives it new abilities it didn't have before. Build effective agents using Model Context Protocol and simple workflow patterns. Once connected, just ask your AI to use it. It's completely free and works across most major AI apps. This one just landed in the catalog — worth trying while it's fresh.

Tips for getting started

1

Tap "Get" above, pick your AI app, and follow the steps. Most installs take under 30 seconds.

2

Heads up: this needs an API key to work. You'll get one from the service's website (usually free). The setup guide tells you exactly where.

What's New

Version 1.0.06 days ago

Imported from GitHub

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