TypeScript
Build your first MCP Server or MPC App:
```bash
npx create-mcp-use-app@latest
```
Or create a server manually:
```typescript
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**](https://mcp-use.com/docs/typescript/server)
## 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:
```typescript
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`:
```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{props.temperature}° — {props.conditions}
Python
```bash
pip install mcp-use
```
```python
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**](https://mcp-use.com/docs/python/server/index)
---
## Inspector
The mcp-use Inspector lets you test and debug your MCP servers interactively.
**Auto-included** when using `server.listen()`:
```typescript
server.listen(3000);
// Inspector at http://localhost:3000/inspector
```
**Online** when connecting to hosted MCP servers:
Python
```bash
pip install mcp-use langchain-openai
```
```python
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**](./libraries/python/README.md#quick-start)
### TypeScript
```bash
npm install mcp-use @langchain/openai
```
```typescript
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();
```
[**→ Full TypeScript Agent Documentation**](./libraries/typescript/README.md#-quick-start)
Python
```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())
```
[**→ Python Client Documentation**](./libraries/python/README.md#direct-tool-calls-without-llm)
### TypeScript
```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();
```
[**→ TypeScript Client Documentation**](./libraries/typescript/README.md#basic-usage)
