> ## Documentation Index
> Fetch the complete documentation index at: https://docs.orxhestra.com/llms.txt
> Use this file to discover all available pages before exploring further.

# MCP Integration

> Connect to MCP-compatible tool servers and use their tools inside any agent. MCPClient and MCPToolAdapter for seamless integration.

Install with MCP support:

```bash theme={null}
pip install orxhestra[mcp]
```

Connect to any MCP-compatible tool server and use its tools inside any agent.

```mermaid theme={null}
sequenceDiagram
    participant A as LlmAgent
    participant W as WrappedMCPTool
    participant C as MCPClient
    participant S as MCP Server

    Note over C,S: Setup (once)
    C->>S: list_tools()
    S-->>C: [Tool definitions]
    C->>W: MCPToolAdapter wraps each tool

    Note over A,W: Runtime (per LLM tool call)
    A->>W: ainvoke({city: "Copenhagen"})
    W->>C: call_tool("get_weather", {city: "Copenhagen"})
    C->>S: Tool execution request
    S-->>C: CallToolResult [TextContent]
    C-->>W: result
    W-->>A: "Sunny, 22°C"
```

## Usage

```python theme={null}
from orxhestra.integrations.mcp import MCPClient, MCPToolAdapter

# Connect to an MCP server (HTTP or in-memory)
client = MCPClient("http://localhost:8001/mcp")

# Wrap MCP tools as LangChain tools
adapter = MCPToolAdapter(client)
mcp_tools = await adapter.load_tools()

agent = LlmAgent(
    name="MCPAgent",
    model=model,
    tools=mcp_tools,
    instructions="Use the available tools to answer questions.",
)
```

## Testing with in-memory server

For testing, pass a fastMCP server object directly (no HTTP needed):

```python theme={null}
from fastmcp import FastMCP

server = FastMCP("TestServer")

@server.tool
def add(a: int, b: int) -> int:
    """Add two numbers."""
    return a + b

client = MCPClient(server)  # in-memory, no network
adapter = MCPToolAdapter(client)
tools = await adapter.load_tools()
```

`MCPToolAdapter.load_tools()` fetches the tool list from the MCP server and wraps each as a LangChain `BaseTool`.