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