Protein Cross-Database Reference
SkillSearchProtein 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).
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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_accessionfromuniprot-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProtget_gene_metadata_by_gene_namefromncbi-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBIget_xrefs_symbolfromensembl-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensemblretrieve_protein_data_by_pdbcodefromserver-2(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
Workflow
- Get UniProt full entry
- Get NCBI gene data
- Get Ensembl cross-references
- Download PDB structure
Test Case
Input
{
"uniprot_accession": "P04637",
"gene": "TP53",
"pdb_code": "1TUP"
}
Expected Steps
- Get UniProt full entry
- Get NCBI gene data
- Get Ensembl cross-references
- 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