> ## 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.

# Overview

> Build MCP servers with dedalus_mcp

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from dedalus_mcp import MCPServer, tool

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

server = MCPServer("calculator")
server.collect(add)

if __name__ == "__main__":
    import asyncio
    asyncio.run(server.serve())
```

Type hints become JSON Schema automatically. Register tools with `collect()`. Same pattern works for resources and prompts.

<Warning>
  **Server name must match your slug.** The `name` in `MCPServer("my-server")` must match your
  deployment slug and `ctx.dispatch()` calls. This ensures OAuth callbacks and request routing work
  correctly.
</Warning>

## With Dedalus SDK

MCP integration is trivial. Pass servers directly to `mcp_servers`:

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
from dedalus_labs import AsyncDedalus, DedalusRunner

client = AsyncDedalus()
runner = DedalusRunner(client)

# Hosted MCP server (marketplace slug)
response = await runner.run(
    input="Search for authentication docs",
    model="anthropic/claude-sonnet-4-20250514",
    mcp_servers=["your-org/your-server"],
)

# Local MCP server URL
response = await runner.run(
    input="Search for authentication docs",
    model="anthropic/claude-sonnet-4-20250514",
    mcp_servers=["http://localhost:8000/mcp"],
)
```

That's it. The SDK handles connection, tool discovery, and execution.

## Server primitives

MCP servers expose three types of capabilities:

| Primitive                                         | Control    | Description                                |
| ------------------------------------------------- | ---------- | ------------------------------------------ |
| [**Tools**](/sdk/mcp/python/server/tools)         | Model      | Functions the LLM calls during reasoning.  |
| [**Resources**](/sdk/mcp/python/server/resources) | Model/User | Data the LLM can read for context.         |
| [**Prompts**](/sdk/mcp/python/server/prompts)     | User       | Message templates users select and render. |

Tools are model-controlled: the LLM decides when to call them. Prompts are user-controlled: users choose which prompt to run. Resources can be either.

## Additional capabilities

| Capability       | How                                     |
| ---------------- | --------------------------------------- |
| **Progress**     | `ctx.progress()` for long-running tasks |
| **Logging**      | `ctx.info()`, `ctx.debug()`, etc.       |
| **Cancellation** | `ctx.cancelled` flag                    |

## Next

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/sdk/mcp/python/quickstart">
    Build an MCP server in 5 minutes
  </Card>

  <Card title="Tools" icon="wrench" href="/sdk/mcp/python/server/tools">
    Expose functions to AI agents
  </Card>

  <Card title="Prompts" icon="message" href="/sdk/mcp/python/server/prompts">
    Reusable message templates
  </Card>

  <Card title="Resources" icon="database" href="/sdk/mcp/python/server/resources">
    Expose data for agents to read
  </Card>

  <Card title="Examples" icon="code" href="/sdk/mcp/python/examples">
    X, GitHub, Gmail production servers
  </Card>
</CardGroup>