zjunlp/scp-pybiomart

SCP-PyBioMart (SciGraph) MCP client

What this SCP is

PyBiomart is a Pythonic interface library for querying BioMart databases (e.g., Ensembl). It supports semi-automated genomic data extraction for constructing biomedical knowledge graphs, including workflows like disease–gene association prediction using knowledge graph embeddings.

Connection info

• MCP server URL:
  • https://scp.intern-ai.org.cn/api/v1/mcp/37/SciGraph
• Auth header:
  • SCP-HUB-API-KEY: {API-KEY}

Install

pip install mcp

Configure (MCP config JSON)

{
  "mcpServers": {
    "SciGraph": {
      "type": "streamableHttp",
      "description": "这是一款面向科学研究的统一知识查询服务，集成了化学、生物等多个学科领域的知识图谱数据，支持跨学科知识检索、实体关系查询、领域知识问答等操作",
      "url": "https://scp.intern-ai.org.cn/api/v1/mcp/37/SciGraph",
      "headers": {
        "SCP-HUB-API-KEY": "{API-KEY}"
      }
    }
  }
}

Tools

query_cypher

Execute a Cypher query and return JSON results.

Arguments:

• cypher (string, required)
• kg_name (string|null, optional, default null)
• limit (int, optional, default 100)

Example arguments (PyBioMart):

{
  "cypher": "MATCH (e:Experiment:PyBioMart) RETURN e.id as experiment_id",
  "kg_name": "PyBioMart",
  "limit": 5
}

get_kg_statistics

Return graph statistics.

Example arguments:

{ "kg_name": "PyBioMart" }

get_entity_details

Return entity details.

Example arguments:

{ "entity_identifier": "experiment_1", "kg_name": "PyBioMart" }

get_experiment_workflow

Return the full workflow of an experiment.

Example arguments:

{ "experiment_id": "experiment_1" }

Python example (streamable HTTP)

import asyncio
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.session import ClientSession

SERVER_URL = "https://scp.intern-ai.org.cn/api/v1/mcp/37/SciGraph"

async def main():
    transport = streamablehttp_client(
        url=SERVER_URL,
        headers={"SCP-HUB-API-KEY": "sk-xxx"},
    )
    read, write, get_session_id = await transport.__aenter__()

    session_ctx = ClientSession(read, write)
    session = await session_ctx.__aenter__()
    await session.initialize()

    # Example: stats for PyBioMart
    result = await session.call_tool(
        "get_kg_statistics",
        arguments={"kg_name": "PyBioMart"},
    )
    data = json.loads(result.content[0].text)
    print(data)

    await session_ctx.__aexit__(None, None, None)
    await transport.__aexit__(None, None, None)

if __name__ == "__main__":
    asyncio.run(main())

Citation

Vilela, J., Asif, M., Marques, A. R., Santos, J. X., Rasga, C., Vicente, A., & Martiniano, H. (2023). Biomedical knowledge graph embeddings for personalized medicine: Predicting disease-gene associations. Expert Systems, 40(5), e13181. https://doi.org/10.1111/exsy.13181

Reference

For the full scraped page text/schemas, read:

• references/source.md