MCP-Use — Build Full-Stack MCP Apps

What it is

MCP-Use is a full-stack Python framework for building Model Context Protocol (MCP) applications. It provides the SDK, server runtime, and client libraries needed to create MCP servers and applications that work with ChatGPT, Claude, and any MCP-compatible AI agent. MCP-Use handles protocol compliance, tool registration, and message routing.

MCP-Use is for AI developers who want to build MCP servers or full-stack MCP applications without dealing with low-level protocol details.

The project is actively maintained with regular releases and a growing user community. Documentation covers common use cases, and the open-source nature means you can inspect the source code, contribute fixes, and adapt the tool to your specific requirements.

How it saves time or tokens

Implementing the MCP specification from scratch requires handling JSON-RPC transport, tool schema registration, SSE streaming, and authentication. MCP-Use provides all of this as a Python SDK. You define your tools as decorated functions and MCP-Use handles the protocol layer, reducing development time from days to hours.

How to use

1. Install MCP-Use via pip or uv.
2. Define your MCP tools as Python functions with the @tool decorator.
3. Run the MCP server and connect it to your AI agent.

Example

from mcp_use import MCPServer, tool

server = MCPServer(name='my-tools')

@server.tool
def search_database(query: str) -> str:
    '''Search the internal database for matching records.'''
    results = db.search(query)
    return f'Found {len(results)} results: {results[:5]}'

@server.tool
def create_ticket(title: str, description: str) -> str:
    '''Create a support ticket in the issue tracker.'''
    ticket = tracker.create(title=title, description=description)
    return f'Created ticket #{ticket.id}'

if __name__ == '__main__':
    server.run()

Related on TokRepo

• AI Tools for Agents -- Agent frameworks and MCP tools
• MCP Integrations -- MCP server integrations on TokRepo

Common pitfalls

• MCP tool descriptions are sent as part of the prompt to the AI model. Vague descriptions lead to incorrect tool calls. Write precise, specific descriptions for each tool.
• MCP-Use servers run as long-lived processes. If your tools make external API calls, implement proper error handling and timeouts to prevent the server from hanging.
• The MCP protocol version matters. Ensure your MCP-Use version matches the protocol version expected by your AI agent client.

Before adopting this tool, evaluate whether it fits your team's existing workflow. Read the official documentation thoroughly, and start with a small proof-of-concept rather than a full migration. Community forums, GitHub issues, and Stack Overflow are valuable resources when you encounter edge cases not covered in the documentation.