Documentation Index
Fetch the complete documentation index at: https://docs.scmcphub.org/llms.txt
Use this file to discover all available pages before exploring further.
Agent library
You can use MCP servers in any Agent library which support MCP.
If you haven’t used any AI library before, you can try Agno(https://docs.agno.com/introduction)
Here is an simple example how to build a agent using scanpy-mcp within Agno.
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:
"""Run the filesystem agent with the given message."""
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="""
You are a bioinformatician. You are good at Python bioinformatic tool like scanpy, anndata, pandas.
Run tools one by one.
""",
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;"
)
)