Swarms API MCP 服务器入门：把整个 Swarms API 作为工具交给任何 AI 客户端

claude mcp add --transport http swarms https://api.swarms.world/mcp \
    --header "x-api-key: your_api_key_here"

{
  "mcpServers": {
    "swarms": {
      "url": "https://api.swarms.world/mcp",
      "headers": {
        "x-api-key": "your_api_key_here"
      }
    }
  }
}

{
  "mcpServers": {
    "swarms": {
      "url": "https://api.swarms.world/mcp",
      "headers": {
        "x-api-key": "your_api_key_here"
      }
    }
  }
}

curl -sS -X POST https://api.swarms.world/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -H "x-api-key: $SWARMS_API_KEY" \
  -d '{
        "jsonrpc": "2.0",
        "id": 1,
        "method": "tools/call",
        "params": {
          "name": "get_available_models_v1_models_available_get",
          "arguments": {}
        }
      }'

{
  "job_id": "agent-4fc7226da44d00a0bb3667251f560a78",
  "success": true,
  "outputs": [
    {
      "role": "Haiku Probe",
      "content": "A team might split a workflow into several agents so that each agent can specialize in one sub-task, which improves quality and makes the system easier to build, test, and maintain."
    }
  ],
  "usage": {
    "input_tokens": 37,
    "output_tokens": 52,
    "total_tokens": 89,
    "total_cost": 0.000034
  }
}

{
  "content": [
    {
      "type": "text",
      "text": "HTTP 200 OK - POST /v1/agent/completions\n{\n  \"job_id\": \"agent-b0abf28…\",\n  …\n}"
    }
  ],
  "structuredContent": {
    "job_id": "agent-b0abf28…",
    "success": true,
    "outputs": [{ "role": "research-agent", "content": "…" }],
    "usage": { "total_tokens": 69, "total_cost": 0.001192 }
  },
  "isError": false
}

pip install mcp httpx

# mcp_quickstart.py
# Connects to the hosted Swarms MCP server, lists its tools, and runs one agent.
# Requires: pip install mcp httpx
import asyncio
import os

import httpx
from mcp import ClientSession
from mcp.client.streamable_http import streamable_http_client

SERVER = "https://api.swarms.world/mcp"


async def main() -> None:
    api_key = os.environ["SWARMS_API_KEY"]

    # The key travels as a transport header. No tool ever receives it as an
    # argument, so the model driving the tools never sees it.
    async with (
        httpx.AsyncClient(headers={"x-api-key": api_key}, timeout=180) as http_client,
        streamable_http_client(SERVER, http_client=http_client) as (read, write),
        ClientSession(read, write) as session,
    ):
        await session.initialize()

        tools = await session.list_tools()
        print(f"{len(tools.tools)} tools available")
        for tool in tools.tools:
            print(" ", tool.name)

        result = await session.call_tool(
            "run_agent_v1_agent_completions_post",
            {
                "agent_config": {
                    "agent_name": "Probe",
                    "system_prompt": "Answer in one sentence.",
                    "model_name": "claude-haiku-5-5",
                    "max_loops": 1,
                    "max_tokens": 200,
                },
                "task": "Name one reason to split a workflow across several agents.",
            },
        )
        if result.isError:
            raise RuntimeError(result.content[0].text)

        payload = result.structuredContent
        print(payload["outputs"][0]["content"])
        print(f"cost: ${payload['usage']['total_cost']}")


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

export SWARMS_API_KEY=your_key
python mcp_quickstart.py

npm install @modelcontextprotocol/sdk

import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";

const apiKey = process.env.SWARMS_API_KEY;
if (!apiKey) throw new Error("set SWARMS_API_KEY");

// The credential is a transport header. No tool takes it as an argument.
const transport = new StreamableHTTPClientTransport(
  new URL("https://api.swarms.world/mcp"),
  { requestInit: { headers: { "x-api-key": apiKey } } },
);

const client = new Client({ name: "swarms-client", version: "1.0.0" });
await client.connect(transport);

const result = await client.callTool({
  name: "run_swarm_v1_swarm_completions_post",
  arguments: {
    name: "market-analysis",
    swarm_type: "SequentialWorkflow",
    task: "Analyze the market for autonomous delivery robots.",
    max_loops: 1,
    agents: [
      { agent_name: "researcher", system_prompt: "Gather and summarize market facts.", model_name: "gpt-5.4-mini", max_loops: 1, max_tokens: 2000 },
      { agent_name: "critic", system_prompt: "Challenge weak claims in the research above.", model_name: "claude-haiku-5-5", max_loops: 1, max_tokens: 2000 },
    ],
  },
});

if (result.isError) {
  throw new Error((result.content as { text: string }[])[0].text);
}

const payload = result.structuredContent as {
  output: { role: string; content: string }[];
  number_of_agents: number;
  execution_time: number;
  usage: { total_cost: number };
};

for (const message of payload.output) {
  console.log(`--- ${message.role} ---\n${message.content}`);
}
console.log(`agents: ${payload.number_of_agents}, elapsed: ${payload.execution_time}s, cost: $${payload.usage.total_cost}`);

await client.close();

claude mcp add --transport http swarms https://api.swarms.world/mcp \
    --header "x-api-key: $SWARMS_API_KEY"