Virus Genomics Analysis - Analyze virus genomics: NCBI virus dataset, annotation, taxonomy, and literature search. Use this skill for virology tasks involving get virus dataset report get virus annotation report get taxonomy search literature. Combines 4 tools from 2 SCP server(s).
Discipline: Virology | Tools Used: 4 | Servers: 2
Analyze virus genomics: NCBI virus dataset, annotation, taxonomy, and literature search.
get_virus_dataset_report from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBIget_virus_annotation_report from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBIget_taxonomy from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBIsearch_literature from server-1 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory{
"accession": "NC_045512.2",
"taxon": "SARS-COV-2"
}
Note: Replace
sk-b04409a1-b32b-4511-9aeb-22980abdc05cwith your own SCP Hub API Key. You can obtain one from the SCP Platform.
import asyncio
import json
from contextlib import AsyncExitStack
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client
SERVERS = {
"ncbi-server": "https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI",
"server-1": "https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory"
}
async def connect(url, stack):
transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"})
read, write, _ = await stack.enter_async_context(transport)
ctx = ClientSession(read, write)
session = await stack.enter_async_context(ctx)
await session.initialize()
return session
def parse(result):
try:
if hasattr(result, 'content') and result.content:
c = result.content[0]
if hasattr(c, 'text'):
try: return json.loads(c.text)
except: return c.text
return str(result)
except: return str(result)
async def main():
async with AsyncExitStack() as stack:
# Connect to required servers
sessions = {}
sessions["ncbi-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", stack)
sessions["server-1"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", stack)
# Execute workflow steps
# Step 1: Get virus dataset report
result_1 = await sessions["ncbi-server"].call_tool("get_virus_dataset_report", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Get virus annotation
result_2 = await sessions["ncbi-server"].call_tool("get_virus_annotation_report", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Get taxonomy info
result_3 = await sessions["ncbi-server"].call_tool("get_taxonomy", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Search recent literature
result_4 = await sessions["server-1"].call_tool("search_literature", arguments={})
data_4 = parse(result_4)
print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")
# Cleanup
print("Workflow complete!")
if __name__ == "__main__":
asyncio.run(main())