MCP Parallel Agent

Create an agent that uses Parallel for searching information using the Parallel MCP server.

Web search using Parallel’s MCP server. API key is optional (keyless access is rate-limited).

Example

import asyncio
from datetime import timedelta
from os import getenv

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.mcp import MCPTools
from agno.tools.mcp.params import StreamableHTTPClientParams


async def run_agent(message: str) -> None:
    headers: dict[str, str] = {}
    api_key = getenv("PARALLEL_API_KEY")
    if api_key:
        headers["Authorization"] = f"Bearer {api_key}"

    server_params = StreamableHTTPClientParams(
        url="https://search.parallel.ai/mcp",
        headers=headers,
        timeout=timedelta(seconds=300),
    )

    async with MCPTools(
        transport="streamable-http",
        server_params=server_params,
        include_tools=["web_search", "web_fetch"],
        timeout_seconds=300,
    ) as parallel_mcp_server:
        agent = Agent(
            model=Claude(id="claude-sonnet-4-20250514"),
            tools=[parallel_mcp_server],
            markdown=True,
        )
        await agent.aprint_response(message, stream=True)


if __name__ == "__main__":
    asyncio.run(run_agent("What is the weather in Tokyo?"))

Run the Example

1. Clone and set up

   git clone https://github.com/agno-agi/agno.git
   cd agno
   
2. Create virtual environment

   ./scripts/demo_setup.sh
   source .venvs/demo/bin/activate
   
3. Export API keys

   export ANTHROPIC_API_KEY=***
   export PARALLEL_API_KEY=***
   

   The Parallel API key is optional. Keyless access is rate-limited; setting a key raises the ceiling.

4. Run the example

   python cookbook/91_tools/mcp/parallel.py