mcp-use recently launched!

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^{"Spin-up and aggregate MCP servers through a single endpoint and zero friction."}

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TL;DR: mcp-use is building open-source dev tools and infrastructure for MCP to help dev teams quickly build and deploy custom AI agents with MCP servers. Their SDK just crossed 100,000 downloads and 4,800 Github stars. Teams at large enterprises like NVIDIA, NASA and SAP are using them to build internal AI tools.
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https://www.youtube.com/watch?v=rU5eQiZFdlI

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Founded by Pietro Zullo & Luigi Pederzani

When MCP and the first use cases came out, they could not believe that such a powerful tech could only be used on IDEs or Claude Desktop.

You can now rebuild any existing AI agent product using just a simple script.

They felt the need to write agents themselves, in code, in a structured and composable way, and allow other developers to do the same. That’s why they first released the mcp-use library.

Now they want to make the development of MCP agents dev-friendly and production-ready for companies.

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Problem

An MCP agent is a set of MCP servers + LLM + system prompt.

With mcp-use library you define the MCP servers config, and initialize the MCP client.

Choose the LLM and system prompt and the agent is ready to go.

async def main():

    load_dotenv()

    client = MCPClient.from_config_file("browser_mcp.json")

    llm = ChatOpenAI(model="gpt-4o")

    agent = MCPAgent(llm=llm, client=client, max_steps=30)

    async for chunk in agent.astream("Look for job at nvidia for machine learning engineer."):

        print(chunk["messages"], end="", flush=True)

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This is all great, but when you have to roll out those agents, you don’t want to share local configurations via email or stitch together pieces of code in your existing codebases.

• You want agent logic to be decoupled and testable.
• A single place to manage configs, auth and access control.

What they have built at mcp-use solves both.

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Demo

https://www.youtube.com/watch?v=BbgmUpaQC_s

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Concrete Use Cases

• NASA is using mcp-use to build their internal agent called “MADI”.
• A famous YC company developed an internal incident management agent with multiple MCP servers (Incidents io, Grafana, Slack). However, the rollout is blocked because they need a central place to manage authentication and access control for tools and resources.
• Their customers integrate production-ready AI agents directly into their products, replacing manual processes. They get their hands dirty with them to provide the infra and avoid common pitfalls (e.g., incorrectly wrapping OpenAPI specs).

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ASK

Do you know anyone already using MCP servers internally or building products powered by AI agents?

For the first few companies they will personally create/integrate MCP servers you need, have everything running through their platform, either on cloud or your own infrastructure. They even help you integrate their SDK into your code.

What do you think? Can they help you?

Book a call.

Send an email.

Shoot them a message: +1 (628) 899-2498 or

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Learn More

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^{🌐 Visit mcp-use.com to learn more.}

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^{📧 If you're building an AI agent, the team would love to show you how mcp-use can help you develop and deploy your custom agents. Book a time here, email here or shoot a them a message: +1 (628) 899-2498 or}

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⭐ Give mcp-use a star on Github.}

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^{👣 Follow mcp-use on LinkedIn & X.}

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