Protein Cross-Database Reference

SkillSearch

Protein Cross-Database Reference - Cross-reference protein: UniProt entry, NCBI gene, Ensembl xrefs, and PDB structure search. Use this skill for proteomics tasks involving get uniprotkb entry by accession get gene metadata by gene name get xrefs symbol retrieve protein data by pdbcode. Combines 4 tools from 4 SCP server(s).

Available today. Use it from your connected AI after setup.

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Then ask your AI: use the Protein Cross-Database Reference skill

What this skill tells your AI

The instructions your AI receives, as published by internscience/scp in skills/protein_database_crossref/SKILL.md and read by ahel’s review.

Discipline: Proteomics | Tools Used: 4 | Servers: 4

Description

Cross-reference protein: UniProt entry, NCBI gene, Ensembl xrefs, and PDB structure search.

Tools Used

  • get_uniprotkb_entry_by_accession from uniprot-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt
  • get_gene_metadata_by_gene_name from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI
  • get_xrefs_symbol from ensembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl
  • retrieve_protein_data_by_pdbcode from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool

Workflow

  1. Get UniProt full entry
  2. Get NCBI gene data
  3. Get Ensembl cross-references
  4. Download PDB structure

Test Case

Input

{
    "uniprot_accession": "P04637",
    "gene": "TP53",
    "pdb_code": "1TUP"
}

Expected Steps

  1. Get UniProt full entry
  2. Get NCBI gene data
  3. Get Ensembl cross-references
  4. Download PDB structure

Usage Example

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

import asyncio
import json
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client

SERVERS = {
    "uniprot-server": "https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt",
    "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",
    "server-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool"
}

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["uniprot-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt", "streamable-http")
    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["server-2"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "streamable-http")

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

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

    # Step 3: Get Ensembl cross-references
    result_3 = await sessions["ensembl-server"].call_tool("get_xrefs_symbol", arguments={})
    data_3 = parse(result_3)
    print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

    # Step 4: Download PDB structure
    result_4 = await sessions["server-2"].call_tool("retrieve_protein_data_by_pdbcode", 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())

Signals

GitHub stars
167
Forks
9
Last commit
Jun 2026
Advanced
Catalog kind
skill
Gateway key
protein-database-crossref
Source
github.com/internscience/scp