mcp-use
Full Stack MCP framework for python, build MCP agents, clients, and servers.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later.
- Requires a langchain-compatible LLM provider configured via environment or passed to the agent.
- Low install friction with a pure-wheel distribution.
License · maintenance · safety
MIT (permissive) — MIT license is permissive, allowing commercial and private use with minimal restrictions; you must include a copy of the license in distributions.
last release 2026-03-17 (150 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 420,524 downloads/mo, #6,789 on PyPI
Alternatives
Verify before relying
pip install mcp-use
from mcp_use import MCPAgent
agent = MCPAgent(llm=None)
# Connect to MCP servers and use tools- 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.
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 on it.
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
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.
Requires Python 3.11 or later. Requires a langchain-compatible LLM provider configured via environment or passed to the agent.
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 toolsVerify 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
| License | MIT permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 11 packagesauthlibhttpxjsonschema-pydanticlangchainmcpposthogpydanticpython-dotenvrichscarf-sdkwebsockets |
| Maintenance | Actively maintained 150 days since the last release |
| First released | |
| Downloads | 420,524 / month, #6,789 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Topic :: Software Development :: Libraries :: Python Modules |
Evidence: mcp_use-1.7.0-py3-none-any.whl
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See also fast-agent-mcp · mcp · llama-index-tools-mcp · langchain-mcp-adapters · arcade-mcp · mcpadapt · nvidia-nat-mcp · mcp-proxy-for-aws · fastmcp-slim · toolbox-adk