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

# MCP Servers

> Connect to any model to any MCP server

The Dedalus SDK is a full MCP client. Connect your agents to any server that implements the [Model Context Protocol](https://modelcontextprotocol.io), hosted by you, us, or anyone else.

Local tools handle your custom logic, MCP servers add hosted capabilities (search, databases, SaaS APIs, etc.).

## Connect MCP server in one line

<CodeGroup>
  ```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
  import asyncio
  from dedalus_labs import AsyncDedalus, DedalusRunner
  from dotenv import load_dotenv

  load_dotenv()

  async def main():
      client = AsyncDedalus()
      runner = DedalusRunner(client)

      result = await runner.run(
          input="What's the weather forecast for San Francisco this week?",
          model="anthropic/claude-opus-4-5",
          mcp_servers=["windsornguyen/open-meteo-mcp"],  # Weather forecasts via Open-Meteo
      )

      print(result.final_output)

  if __name__ == "__main__":
      asyncio.run(main())
  ```

  ```typescript TypeScript theme={"theme":{"light":"github-light","dark":"github-dark"}}
  import Dedalus from "dedalus-labs";
  import { DedalusRunner } from "dedalus-labs";

  const client = new Dedalus();
  const runner = new DedalusRunner(client);

  async function main() {
  	const result = await runner.run({
  		input: "What's the weather forecast for San Francisco this week?",
  		model: "anthropic/claude-opus-4-5",
  		mcpServers: ["windsornguyen/open-meteo-mcp"], // Weather forecasts via Open-Meteo
  	});

  	console.log(result.finalOutput);
  }

  main();
  ```
</CodeGroup>

The agent discovers the server's tools and uses them when relevant.

## Combine with local tools

MCP servers and local tools work together. Pass both to `runner.run()`.

<CodeGroup>
  ```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
  import asyncio
  from dedalus_labs import AsyncDedalus, DedalusRunner
  from dotenv import load_dotenv

  load_dotenv()

  def as_bullets(items: list[str]) -> str:
      """Format items as a bulleted list."""
      return "\n".join(f"• {item}" for item in items)

  async def main():
      client = AsyncDedalus()
      runner = DedalusRunner(client)

      result = await runner.run(
          input=(
              "Get the 7-day weather forecast for San Francisco "
              "and format the daily conditions as bullets using as_bullets."
          ),
          model="anthropic/claude-opus-4-5",
          mcp_servers=["windsornguyen/open-meteo-mcp"],
          tools=[as_bullets],
      )

      print(result.final_output)

  if __name__ == "__main__":
      asyncio.run(main())
  ```

  ```typescript TypeScript theme={"theme":{"light":"github-light","dark":"github-dark"}}
  import Dedalus from "dedalus-labs";
  import { DedalusRunner } from "dedalus-labs";

  const client = new Dedalus();
  const runner = new DedalusRunner(client);

  function asBullets(items: string[]): string {
  	return items.map((item) => `• ${item}`).join("\n");
  }

  async function main() {
  	const result = await runner.run({
  		input:
  			"Get the 7-day weather forecast for San Francisco and format the daily conditions as bullets using asBullets.",
  		model: "anthropic/claude-opus-4-5",
  		mcpServers: ["windsornguyen/open-meteo-mcp"],
  		tools: [asBullets],
  	});

  	console.log((result as any).finalOutput);
  }

  main();
  ```
</CodeGroup>

## External MCP URL

You can connect directly to any external MCP server URL (Streamable HTTP). This is useful when:

* You’re testing a server without registering it
* You’re connecting to a self-hosted MCP deployment
* You’re using an MCP server that isn’t in the marketplace

<CodeGroup>
  ```python Python theme={"theme":{"light":"github-light","dark":"github-dark"}}
  import asyncio
  from dedalus_labs import AsyncDedalus, DedalusRunner
  from dotenv import load_dotenv

  load_dotenv()

  async def main():
      client = AsyncDedalus()
      runner = DedalusRunner(client)

      result = await runner.run(
          input="Use your tools to summarize the Dedalus Python SDK repo in 5 bullet points.",
          model="openai/gpt-5.2",
          # External MCP URL!
          mcp_servers=["https://mcp.deepwiki.com/mcp"],
      )

      print(result.final_output)

  if __name__ == "__main__":
      asyncio.run(main())
  ```

  ```typescript TypeScript theme={"theme":{"light":"github-light","dark":"github-dark"}}
  import Dedalus from "dedalus-labs";
  import { DedalusRunner } from "dedalus-labs";

  const client = new Dedalus();
  const runner = new DedalusRunner(client);

  async function main() {
  	const result = await runner.run({
  		input: "Use your tools to summarize the Dedalus Python SDK repo in 5 bullet points.",
  		model: "openai/gpt-5.2",
  		// External MCP URL!
  		mcpServers: ["https://mcp.deepwiki.com/mcp"],
  	});

  	console.log((result as any).finalOutput);
  }

  main();
  ```
</CodeGroup>

## Next steps

<CardGroup cols={2}>
  <Card title="Structured Outputs" href="/sdk/agents/structured-outputs/typescript">
    Validate and parse JSON into schemas
  </Card>

  <Card title="Streaming" href="/sdk/agents/streaming/typescript">
    Watch tool use + output in real time
  </Card>

  <Card title="Use Cases" href="/sdk/agents/use-cases/web-search-agent/typescript">
    End-to-end MCP agent patterns
  </Card>
</CardGroup>