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internscience/scp/gene-comprehensive-lookup

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PublishedJune 16, 2026 at 11:17 PM

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version: "1.0.0" name: gene_comprehensive_lookup description: "Gene Comprehensive Lookup - Comprehensive gene lookup: NCBI gene data, Ensembl gene info, UniProt protein data, and KEGG pathway links. Use this skill for bioinformatics tasks involving get gene metadata by gene name get lookup symbol get general info by protein or gene name kegg find. Combines 4 tools from 4 SCP server(s)."

Gene Comprehensive Lookup

Discipline: Bioinformatics | Tools Used: 4 | Servers: 4

Description

Comprehensive gene lookup: NCBI gene data, Ensembl gene info, UniProt protein data, and KEGG pathway links.

Tools Used

`get_gene_metadata_by_gene_name` from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI
`get_lookup_symbol` from ensembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl
`get_general_info_by_protein_or_gene_name` from uniprot-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt
`kegg_find` from kegg-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG

Workflow

Get NCBI gene metadata
Look up in Ensembl
Get UniProt protein info
Find in KEGG

Test Case

Input

json

{
    "gene_name": "BRCA1",
    "species": "homo_sapiens"
}

Expected Steps

Get NCBI gene metadata
Look up in Ensembl
Get UniProt protein info
Find in KEGG

Usage Example

Note: Replace <YOUR_SCP_HUB_API_KEY> with your own SCP Hub API Key. You can obtain one from the SCP Platform.

python

import asyncio
import json
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",
    "uniprot-server": "https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt",
    "kegg-server": "https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG"
}
async def connect(url, transport_type):
    transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "<YOUR_SCP_HUB_API_KEY>"})
    read, write, _ = await transport.__aenter__()
    ctx = ClientSession(read, write)
    session = await ctx.__aenter__()
    await session.initialize()
    return session, ctx, transport
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():
    # Connect to required servers
    sessions = {}
    sessions["ncbi-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", "streamable-http")
    sessions["ensembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", "streamable-http")
    sessions["uniprot-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt", "streamable-http")
    sessions["kegg-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG", "streamable-http")
    # Execute workflow steps
    # Step 1: Get NCBI gene metadata
    result_1 = await sessions["ncbi-server"].call_tool("get_gene_metadata_by_gene_name", arguments={})
    data_1 = parse(result_1)
    print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
    # Step 2: Look up in Ensembl
    result_2 = await sessions["ensembl-server"].call_tool("get_lookup_symbol", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
    # Step 3: Get UniProt protein info
    result_3 = await sessions["uniprot-server"].call_tool("get_general_info_by_protein_or_gene_name", arguments={})
    data_3 = parse(result_3)
    print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
    # Step 4: Find in KEGG
    result_4 = await sessions["kegg-server"].call_tool("kegg_find", 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())

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