mcp-use

What it is and what it does

mcp-use is a Python framework that bridges LLMs and the Model Context Protocol (MCP), a standard for connecting AI models to external tools and data sources. It provides three main components: an MCP Agent that reasons across multiple steps and selects tools to solve problems, an MCP Client for direct programmatic access to MCP servers, and an MCP Server builder for creating and deploying custom servers.

The package is designed for developers building AI applications that need tool access. It works with langchain-supported LLMs that support tool calling and handles multiple MCP servers simultaneously, allowing agents to dynamically select the appropriate server for a given task. It includes features like tool restrictions for safety, HTTP support for remote servers, and custom agent building via langchain adapters.

Use it for:

• Build an AI agent that can call multiple external APIs or services by connecting them as MCP servers.
• Create a chatbot that dynamically selects and executes tools from a pool of MCP servers based on user requests.
• Develop a custom MCP server to expose internal tools or data sources to LLM-powered applications.
• Integrate file system, database, or network tools into an LLM workflow with built-in safety restrictions.
• Prototype AI agent logic using the interactive code builder before deploying to production.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

mcp-use provides a Python framework to connect LLMs to Model Context Protocol servers, enabling agents to discover and call tools, clients to access MCP servers directly, and developers to build and deploy MCP servers.

Yes, if you are building LLM-powered applications that need tool access and want a structured framework for MCP integration. The low install friction, active maintenance, permissive license, and support for multiple LLM providers make it a solid choice. The Alpha status and 11 dependencies warrant testing in a non-critical environment first, but the lack of known vulnerabilities and recent release cycle suggest reasonable stability for early adoption.

Install

mcp-use on PyPI

pip install mcp-use

uv add mcp-use

poetry add mcp-use

Installing mcp-use

Before you install

Low install friction with a pure-wheel distribution. Actively maintained with a recent release. Requires 11 runtime dependencies including langchain, pydantic, and httpx, which are themselves stable and widely used.

License in practice

MIT license is permissive, allowing commercial and private use with minimal restrictions; you must include a copy of the license in distributions.

Quickstart

pip install mcp-use

from mcp_use import MCPAgent

agent = MCPAgent(llm=None)
# Connect to MCP servers and use tools

Requires Python 3.11 or later. Requires a langchain-compatible LLM provider configured via environment or passed to the agent.

Verify before relying

• Whether the 'six lines of code' claim for a first agent is accurate or marketing language.
• Specific performance characteristics or latency for multi-server agent scenarios.
• Production readiness status beyond the 'Alpha' development classifier.
• Whether tool restrictions (file system, network access) are enforced at runtime or advisory only.
• Which specific langchain-supported LLM providers are tested and recommended.

Package facts

Evidence: mcp_use-1.7.0-py3-none-any.whl

Tags

More Python Modules packages