Basic MCP

Use MCP servers to expose external tools/resources to Draive through MCPClient.

This pattern is useful when you want models to call capabilities provided by external MCP servers without writing custom SDK integration code.

Example: Filesystem MCP Server

from draive import Conversation, ToolsProvider, ctx, load_env, setup_logging
from draive.mcp import MCPClient
from draive.openai import OpenAI, OpenAIResponsesConfig

load_env()
setup_logging("mcp")

async with ctx.scope(
    "mcp",
    OpenAIResponsesConfig(model="gpt-5.5"),
    disposables=(
        OpenAI(),
        # Start MCP stdio transport and register tool/resource states in context.
        MCPClient.stdio(
            command="npx",
            args=[
                "-y",
                "@modelcontextprotocol/server-filesystem",
                "/Users/myname/checkmeout",
            ],
        ),
    ),
):
    # Build toolbox from currently available MCP tools.
    toolbox = await ToolsProvider.toolbox(suggesting=True)

    stream = Conversation.completion(
        instructions=(
            "You can access user files using available tools. "
            "Directory path is /Users/myname/checkmeout."
        ),
        message="What files are in checkmeout directory?",
        tools=toolbox,
    )

    async for chunk in stream:
        print(chunk)

MCPClient contributes ToolsProvider/ResourcesRepository states inside ctx.scope(...), so you can load MCP tools dynamically and keep all dependencies lifecycle-managed by context.

Example: Remote MCP Server

For remote servers use the Streamable HTTP transport. MCPClient.sse is still available but the SSE transport is deprecated by the protocol, prefer Streamable HTTP for new integrations.

from draive.mcp import MCPClient

MCPClient.streamable_http(
    url="https://example.com/mcp",
    headers={"Authorization": "Bearer <token>"},
)

Exposing Draive tools and resources the other way around works through MCPServer, which serves either stdio or an ASGI app:

from draive.mcp import MCPServer

server = MCPServer(name="my-server", version="1.0.0", tools=[my_tool])

# stdio for local subprocess usage
await server.run_stdio()

# or an ASGI app to serve over Streamable HTTP
app = server.prepare_streamable_http_asgi(path="/mcp")