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scp-opb

scp-opb

【开发与工程】Use when you need to connect to the SciGraph SCP server for OPB (Ontology of Physics for Biology; reference ontology applying physics/thermodynamics concepts to biological systems across scales) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-AP…

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技能说明

GitHub

name: scp-opb description: Use when you need to connect to the SciGraph SCP server for OPB (Ontology of Physics for Biology; reference ontology applying physics/thermodynamics concepts to biological systems across scales) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.

SCP-OPB (SciGraph) MCP client

What this SCP is

OPB (Ontology of Physics for Biology) is a reference ontology that applies classical physics and thermodynamic principles to the study of biological system dynamics. It is intended to cover multiple structural scales (atoms → organisms) and physical domains (fluid dynamics, chemical kinetics, diffusion, etc.) used in biological research and analysis.

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 (OPB):

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

get_kg_statistics

Return graph statistics.

Example arguments:

{ "kg_name": "OPB" }

get_entity_details

Return entity details.

Example arguments:

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

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 OPB
    result = await session.call_tool(
        "get_kg_statistics",
        arguments={"kg_name": "OPB"},
    )
    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

Cook, D. L., Bookstein, F. L., & Gennari, J. H. (2011). Physical properties of biological entities: An introduction to the ontology of physics for biology. PLOS ONE, 6(12), e28708. https://doi.org/10.1371/journal.pone.0028708

Reference

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  • references/source.md
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