zjunlp/scp-ddkg

SCP-DDKG (SciGraph) MCP client

What this SCP is

DDKG (Attention-based Knowledge Graph Representation Learning for Predicting Drug-drug Interactions) is a biomedical KG for predicting drug–drug interactions (DDIs), integrating drug chemical structures (SMILES) with biomedical triples (e.g., drug–disease, drug–protein).

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): if omitted, queries across all graphs.
• limit (int, optional, default 100)

Example arguments (DDKG):

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

get_kg_statistics

Return graph statistics.

Arguments:

• kg_name (string|null, optional): omit for all.

Example arguments (DDKG):

{ "kg_name": "DDKG" }

get_entity_details

Return entity details (works across graphs).

Arguments:

• entity_identifier (string, required)
• kg_name (string|null, optional)

Example arguments (DDKG):

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

get_experiment_workflow

Return the full workflow of an experiment.

Arguments:

• experiment_id (string, required)

Example:

{ "experiment_id": "experiment_1" }

Python example (streamable HTTP)

Pattern: streamablehttp_client + ClientSession.

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

Su, X., Hu, L., You, Z., Hu, P., & Zhao, B. (2022). Attention-based knowledge graph representation learning for predicting drug-drug interactions. Briefings in Bioinformatics, 23(3), bbac140. https://doi.org/10.1093/bib/bbac140

Reference

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• references/source.md