MCP

A hosted Model Context Protocol server for the Swarms API. Point any MCP client at one URL to give it agents, swarms, and batch execution as callable tools. No local process to run.

Endpoint

https://mcp.swarms.world/mcp

Checking

Status

Checking…

Probing the endpoint from our region.

Handshake latency

—

Measured server-side from our region.

Observed uptime

—

No observations recorded yet on this server.

Incident history

Connect

Open a session and list the available tools.

The server speaks streamable HTTP and authenticates with the same x-api-key header as the REST API. Create a key in API keys and export it as SWARMS_API_KEY.

# pip install mcp
import asyncio
import os

from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

SWARMS_MCP_URL = "https://mcp.swarms.world/mcp"


async def main() -> None:
    async with streamablehttp_client(
        SWARMS_MCP_URL,
        headers={"x-api-key": os.environ["SWARMS_API_KEY"]},
    ) as (read, write, _):
        async with ClientSession(read, write) as session:
            await session.initialize()

            # Every tool the server exposes, with its input schema.
            tools = await session.list_tools()
            for tool in tools.tools:
                print(tool.name)


asyncio.run(main())

Tools

Everything the server exposes.

Reading the tool list from the server…

Run a swarm

Fan one task across a team of agents.

Call the swarm completions tool through the session you just opened. This example runs a ConcurrentWorkflow so both analysts work the same task in parallel and return together. Tool names come from list_tools, so run the connect snippet first to confirm what your key can reach.

import asyncio
import json
import os

from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

SWARMS_MCP_URL = "https://mcp.swarms.world/mcp"


async def main() -> None:
    async with streamablehttp_client(
        SWARMS_MCP_URL,
        headers={"x-api-key": os.environ["SWARMS_API_KEY"]},
    ) as (read, write, _):
        async with ClientSession(read, write) as session:
            await session.initialize()

            result = await session.call_tool(
                "run_swarm_v1_swarm_completions_post",
                {
                    "name": "Market Research Swarm",
                    "description": "Three analysts research the same task in parallel",
                    "swarm_type": "ConcurrentWorkflow",
                    "task": "Analyze the impact of AI agents on modern healthcare",
                    "agents": [
                        {
                            "agent_name": "Market Analyst",
                            "system_prompt": "You analyze market trends and opportunities.",
                            "model_name": "gpt-5.4",
                            "max_loops": 1,
                        },
                        {
                            "agent_name": "Risk Analyst",
                            "system_prompt": "You identify risks and regulatory constraints.",
                            "model_name": "claude-haiku-4-5",
                            "max_loops": 1,
                        },
                    ],
                    "max_loops": 1,
                },
            )

            for block in result.content:
                if block.type == "text":
                    print(json.dumps(json.loads(block.text), indent=2))


asyncio.run(main())

The same swarm without MCP, straight against the REST endpoint:

Swarm completion over HTTP

POST /v1/swarm/completions

import os
import requests

payload = {
    "name": "Market Research Swarm",
    "description": "Three analysts research the same task in parallel",
    "swarm_type": "ConcurrentWorkflow",
    "task": "Analyze the impact of AI agents on modern healthcare",
    "agents": [
        {
            "agent_name": "Market Analyst",
            "system_prompt": "You analyze market trends and opportunities.",
            "model_name": "gpt-5.4",
            "max_loops": 1
        },
        {
            "agent_name": "Risk Analyst",
            "system_prompt": "You identify risks and regulatory constraints.",
            "model_name": "claude-haiku-4-5",
            "max_loops": 1
        }
    ],
    "max_loops": 1
}

response = requests.post(
    "https://api.swarms.world/v1/swarm/completions",
    headers={
        "x-api-key": os.environ["SWARMS_API_KEY"],
        "Content-Type": "application/json",
    },
    json=payload,
)
response.raise_for_status()
print(response.json())

Tutorials

Worked examples, end to end.

TypeScript

Documentation

Reference and machine-readable index.

MCP client reference

Transport, authentication, and every tool the server exposes.

docs.swarms.ai/llms.txt

The full documentation index in one plaintext file, for agents that crawl it directly.

MCP