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organism_classification

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Organism Classification & Database - Classify organism: NCBI taxonomy, Ensembl taxonomy, ChEMBL organisms, and genome info. Use this skill for taxonomy tasks involving get taxonomy get taxonomy id get organism by id get genome dataset report by taxon. Combines 4 tools from 3 SCP server(s).

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Organism Classification & Database

Discipline: Taxonomy | Tools Used: 4 | Servers: 3

Description

Classify organism: NCBI taxonomy, Ensembl taxonomy, ChEMBL organisms, and genome info.

Tools Used

  • get_taxonomy from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI
  • get_taxonomy_id from ensembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl
  • get_organism_by_id from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL
  • get_genome_dataset_report_by_taxon from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI

Workflow

  1. Get NCBI taxonomy
  2. Get Ensembl taxonomy
  3. Get ChEMBL organism info
  4. Get genome dataset report

Test Case

Input

{
    "taxon": "9606",
    "species": "homo_sapiens"
}

Expected Steps

  1. Get NCBI taxonomy
  2. Get Ensembl taxonomy
  3. Get ChEMBL organism info
  4. Get genome dataset report

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",
    "ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl",
    "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL"
}

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["ensembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", stack)
        sessions["chembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", stack)

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

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

        # Step 3: Get ChEMBL organism info
        result_3 = await sessions["chembl-server"].call_tool("get_organism_by_id", 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 dataset report
        result_4 = await sessions["ncbi-server"].call_tool("get_genome_dataset_report_by_taxon", 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())