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microbiome_genomics

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Microbiome Genomics Analysis - Analyze microbial genome: NCBI genome data, taxonomy, KEGG metabolic pathways, and annotation. Use this skill for metagenomics tasks involving get genome dataset report by taxon get taxonomy kegg find get genome annotation report. Combines 4 tools from 2 SCP server(s).

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Microbiome Genomics Analysis

Discipline: Metagenomics | Tools Used: 4 | Servers: 2

Description

Analyze microbial genome: NCBI genome data, taxonomy, KEGG metabolic pathways, and annotation.

Tools Used

  • get_genome_dataset_report_by_taxon from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI
  • get_taxonomy from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI
  • kegg_find from kegg-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG
  • get_genome_annotation_report from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI

Workflow

  1. Get genome dataset for E. coli
  2. Get taxonomic classification
  3. Find KEGG metabolic pathways
  4. Get genome annotation

Test Case

Input

{
    "taxon": "Escherichia coli",
    "accession": "GCF_000005845.2"
}

Expected Steps

  1. Get genome dataset for E. coli
  2. Get taxonomic classification
  3. Find KEGG metabolic pathways
  4. Get genome annotation

Usage Example

Note: Replace sk-b04409a1-b32b-4511-9aeb-22980abdc05c with 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",
    "kegg-server": "https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG"
}

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["kegg-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG", stack)

        # Execute workflow steps
        # Step 1: Get genome dataset for E. coli
        result_1 = await sessions["ncbi-server"].call_tool("get_genome_dataset_report_by_taxon", arguments={})
        data_1 = parse(result_1)
        print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

        # Step 2: Get taxonomic classification
        result_2 = await sessions["ncbi-server"].call_tool("get_taxonomy", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

        # Step 3: Find KEGG metabolic pathways
        result_3 = await sessions["kegg-server"].call_tool("kegg_find", arguments={})
        data_3 = parse(result_3)
        print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

        # Step 4: Get genome annotation
        result_4 = await sessions["ncbi-server"].call_tool("get_genome_annotation_report", 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())