Unified MCP Client Library
An open-source library to connect any LLM to any MCP server, enabling the creation of custom agents with tool access.
About
mcp-use is the fullstack MCP framework to build MCP Apps for ChatGPT / Claude & MCP Servers for AI Agents.
- Build with mcp-use SDK (ts | py): MCP Servers and MCP Apps
- Preview on mcp-use MCP Inspector (online | oss): Test and debug your MCP Servers and Apps
- Deploy on Manufact MCP Cloud: Connect your GitHub repo and have your MCP Server and App up and running in production with observability, metrics, logs, branch-deployments, and more
Documentation
Visit our docs or jump to a quickstart (TypeScript | Python)
Skills for Coding Agents
Using Claude Code, Codex, Cursor or other AI coding agents?
Quickstart: MCP Servers and MCP Apps
TypeScript
Build your first MCP Server or MPC App:
npx create-mcp-use-app@latest
Or create a server manually:
import { MCPServer, text } from "mcp-use/server";
import { z } from "zod";
const server = new MCPServer({
name: "my-server",
version: "1.0.0",
});
server.tool({
name: "get_weather",
description: "Get weather for a city",
schema: z.object({ city: z.string() }),
}, async ({ city }) => {
return text(`Temperature: 72°F, Condition: sunny, City: ${city}`);
});
await server.listen(3000);
// Inspector at http://localhost:3000/inspector
→ Full TypeScript Server Documentation
MCP Apps
MCP Apps let you build interactive widgets that work across Claude, ChatGPT, and other MCP clients — write once, run everywhere.
Server: define a tool and point it to a widget:
import { MCPServer, widget } from "mcp-use/server";
import { z } from "zod";
const server = new MCPServer({
name: "weather-app",
version: "1.0.0",
});
server.tool({
name: "get-weather",
description: "Get weather for a city",
schema: z.object({ city: z.string() }),
widget: "weather-display", // references resources/weather-display/widget.tsx
}, async ({ city }) => {
return widget({
props: { city, temperature: 22, conditions: "Sunny" },
message: `Weather in ${city}: Sunny, 22°C`,
});
});
await server.listen(3000);
Widget: create a React component in resources/weather-display/widget.tsx:
import { useWidget, type WidgetMetadata } from "mcp-use/react";
import { z } from "zod";
const propSchema = z.object({
city: z.string(),
temperature: z.number(),
conditions: z.string(),
});
export const widgetMetadata: WidgetMetadata = {
description: "Display weather information",
props: propSchema,
};
const WeatherDisplay: React.FC = () => {
const { props, isPending, theme } = useWidget<z.infer<typeof propSchema>>();
const isDark = theme === "dark";
if (isPending) return <div>Loading...</div>;
return (
<div style={{
background: isDark ? "#1a1a2e" : "#f0f4ff",
borderRadius: 16, padding: 24,
}}>
<h2>{props.city}</h2>
<p>{props.temperature}° — {props.conditions}</p>
</div>
);
};
export default WeatherDisplay;
Widgets in resources/ are auto-discovered — no manual registration needed.
Visit MCP Apps Documentation
Templates
Ready-to-use MCP Apps you can deploy in one click or remix as your own.
| Preview | Name | Tools | Demo URL | Repo | Deploy |
|---|---|---|---|---|---|
![]() | Chart Builder | create-chart | Open URL | mcp-use/mcp-chart-builder | |
![]() | Diagram Builder | create-diagram, edit-diagram | Open URL | mcp-use/mcp-diagram-builder | |
![]() | Slide Deck | create-slides, edit-slide | Open URL | mcp-use/mcp-slide-deck | |
![]() | Maps Explorer | show-map, get-place-details, add-markers | Open URL | mcp-use/mcp-maps-explorer | |
![]() | Hugging Face Spaces | search-spaces, show-space, trending-spaces | Open URL | mcp-use/mcp-huggingface-spaces | |
![]() | Recipe Finder | search-recipes, get-recipe, meal-plan, recipe-suggestion | Open URL | mcp-use/mcp-recipe-finder | |
![]() | Widget Gallery | show-react-widget, html-greeting, mcp-ui-poll, programmatic-counter, detect-client | Open URL | mcp-use/mcp-widget-gallery | |
![]() | Multi Server Hub | hub-status, hub-config-example, audit-log | Open URL | mcp-use/mcp-multi-server-hub | |
![]() | File Manager | open-vault, get-file, list-files | Open URL | mcp-use/mcp-file-manager | |
![]() | Progress Demo | process-data, fetch-report, delete-dataset, search-external, failing-tool | Open URL | mcp-use/mcp-progress-demo | |
![]() | i18n Adaptive | show-context, detect-caller | Open URL | mcp-use/mcp-i18n-adaptive | |
![]() | Media Mixer | generate-image, generate-audio, generate-pdf, get-report, get-html-snippet, get-xml-config, get-stylesheet, get-script, get-data-array | Open URL | mcp-use/mcp-media-mixer | |
![]() | Resource Watcher | show-config, update-config, toggle-feature, list-roots | Open URL | mcp-use/mcp-resource-watcher |
Python
pip install mcp-use
from typing import Annotated
from mcp.types import ToolAnnotations
from pydantic import Field
from mcp_use import MCPServer
server = MCPServer(name="Weather Server", version="1.0.0")
@server.tool(
name="get_weather",
description="Get current weather information for a location",
annotations=ToolAnnotations(readOnlyHint=True, openWorldHint=True),
)
async def get_weather(
city: Annotated[str, Field(description="City name")],
) -> str:
return f"Temperature: 72°F, Condition: sunny, City: {city}"
# Start server with auto-inspector
server.run(transport="streamable-http", port=8000)
# 🎉 Inspector at http://localhost:8000/inspector
→ Full Python Server Documentation
Inspector
The mcp-use Inspector lets you test and debug your MCP servers interactively.
Auto-included when using server.listen():
server.listen(3000);
// Inspector at http://localhost:3000/inspector
Online when connecting to hosted MCP servers:
Standalone: inspect any MCP server:
npx @mcp-use/inspector --url http://localhost:3000/mcp
Visit Inspector Documentation
Deploy
Deploy your MCP server to production:
npx @mcp-use/cli login
npx @mcp-use/cli deploy
Or connect your GitHub repo on manufact.com — production-ready with observability, metrics, logs, and branch-deployments.
Package Overview
This monorepo contains multiple packages for both Python and TypeScript:
Python Packages
| Package | Description | Version |
|---|---|---|
| mcp-use | Complete MCP server and MCP agent SDK |
TypeScript Packages
Also: MCP Agent & Client
mcp-use also provides a full MCP Agent and Client implementation.
Build an AI Agent
Python
pip install mcp-use langchain-openai
import asyncio
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient
async def main():
config = {
"mcpServers": {
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"]
}
}
}
client = MCPClient.from_dict(config)
llm = ChatOpenAI(model="gpt-4o")
agent = MCPAgent(llm=llm, client=client)
result = await agent.run("List all files in the directory")
print(result)
asyncio.run(main())
→ Full Python Agent Documentation
TypeScript
npm install mcp-use @langchain/openai
import { ChatOpenAI } from "@langchain/openai";
import { MCPAgent, MCPClient } from "mcp-use";
async function main() {
const config = {
mcpServers: {
filesystem: {
command: "npx",
args: ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"],
},
},
};
const client = MCPClient.fromDict(config);
const llm = new ChatOpenAI({ modelName: "gpt-4o" });
const agent = new MCPAgent({ llm, client });
const result = await agent.run("List all files in the directory");
console.log(result);
}
main();
Use MCP Client
Python
import asyncio
from mcp_use import MCPClient
async def main():
config = {
"mcpServers": {
"calculator": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-everything"]
}
}
}
client = MCPClient.from_dict(config)
await client.create_all_sessions()
session = client.get_session("calculator")
result = await session.call_tool(name="add", arguments={"a": 5, "b": 3})
print(f"Result: {result.content[0].text}")
await client.close_all_sessions()
asyncio.run(main())
TypeScript
import { MCPClient } from "mcp-use";
async function main() {
const config = {
mcpServers: {
calculator: {
command: "npx",
args: ["-y", "@modelcontextprotocol/server-everything"],
},
},
};
const client = new MCPClient(config);
await client.createAllSessions();
const session = client.getSession("calculator");
const result = await session.callTool("add", { a: 5, b: 3 });
console.log(`Result: ${result.content[0].text}`);
await client.closeAllSessions();
}
main();
Conformance to Model Context Protocol
Security
See SECURITY.md
Community & Support
- Discord: Join our community
- GitHub Issues: Report bugs or request features
- Documentation: mcp-use.com/docs
- Website: manufact.com
- X.com: Follow Manufact
- Contributing: See CONTRIBUTING.md
- License: MIT © MCP-Use Contributors
Star History
Contributors
Thanks to all our amazing contributors!
Core Contributors
- Pietro (@pietrozullo)
- Luigi (@pederzh)
- Enrico (@tonxxd)
San Francisco | Zürich
Похожие серверы
Alpha Vantage MCP Server
спонсорAccess financial market data: realtime & historical stock, ETF, options, forex, crypto, commodities, fundamentals, technical indicators, & more
atlassian-browser-mcp
rowser-backed MCP wrapper for mcp-atlassian with Playwright SSO auth. Enables AI tools to access Atlassian Server/Data Center instances behind corporate SSO (Okta, SAML, ADFS) where API tokens are not available.
Juniper Junos MCP Server
An MCP server for interacting with Juniper Junos network devices using LLMs.
AST2LLM for Go
An AST-powered tool that enhances LLM context by automatically injecting relevant Go code structures into prompts.
MCP Memory Keeper
A server for persistent context management in Claude AI coding assistants, using a local SQLite database for storage.
OpenRouter MCP Client for Cursor
An MCP client for Cursor that uses OpenRouter.ai to access multiple AI models. Requires an OpenRouter API key.
Code Context Provider MCP
Provides code context and analysis for AI assistants using WebAssembly Tree-sitter parsers.
Icons8 MCP server
Get access to MCP server SVG and MCP server PNG icons in your vibe-coding projects
Woodpecker MCP Server
A server for managing Woodpecker CI/CD pipelines, built with the MCP framework.
agent-audit
Security scanner for MCP servers and AI agent tooling. Detects prompt injection, command injection, auth bypass, and excessive permissions.
Terry-Form MCP
Execute Terraform commands locally in a secure, containerized environment. Features LSP integration for intelligent Terraform development.












