使用 MCP 构建 agent

import asyncio

from agno.agent import Agent
from agno.tools.mcp import MCPTools
from agno.models.openai.like import OpenAILike

model = OpenAILike(
        id="qwen-plus",
        api_key="sk-**",
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
        extra_query={"enable_thinking": False},
    )


async def run_agent(message: str) -> None:
    """使用给定的消息运行文件系统 agent。"""
    async with MCPTools(
            "scanpy-mcp run --run-mode tool", timeout_seconds=60,
    ) as mcp_tools:        
        agent = Agent(
            model= model, 
            tools=[mcp_tools],
            show_tool_calls=True,
            debug_mode=True,
            description="""
            你是一个生物信息学家。你擅长使用 Python 生物信息学工具，如 scanpy、anndata、pandas。
            请逐个运行工具。
             """,
              monitoring=True
        )
        await agent.aprint_response(message, stream=True)



if __name__ == "__main__":

    asyncio.run(
        run_agent(
            "read /data20T/dev/scmcphub/scanpy-mcp/tests/data/hg19; "
            "then filter cells which gene number < 500; filter genes which express < 3 cells;"
            "compute qc metrics,include mitochondrial and ribosomal genes, plot total counts, 线粒体百分比，gene number 的小提琴图, 质控图使用multiple_pannel,;"
            "normalize and Logarithmize the data;"
            "Identify highly-variable genes; Reduce the dimensionality， 绘制pca_variance_ratio;"
            "perform cell clustering,设置resolution=0.5， 添加key leiden.0.5, "
            "and draw UMAP and TSNE scatter plot, color by leiden.0.5, PTPRC, NKG7,KLRD1,GNLY,CST7,PRF1, 每行显示3个图;"
            )
        )