scp-ikraph

name	scp-ikraph
description	Use when you need to connect to the SciGraph SCP server for iKraph (large-scale biomedical knowledge graph from PubMed + external databases like Hetionet) 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-iKraph (SciGraph) MCP client

What this SCP is

iKraph is a comprehensive, large-scale biomedical knowledge graph for AI-powered and data-driven biomedical research. It integrates structured relationships extracted from PubMed abstracts and external biomedical databases (e.g., Hetionet), covering entities such as genes, drugs, chemicals, and diseases.

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 (iKraph):

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

get_kg_statistics

Return graph statistics.

Example arguments:

{ "kg_name": "iKraph" }

get_entity_details

Return entity details.

Example arguments:

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

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

Zhang, Y., Sui, X., Pan, F. et al. A comprehensive large-scale biomedical knowledge graph for AI-powered data-driven biomedical research. Nat Mach Intell 7, 602–614 (2025). https://doi.org/10.1038/s42256-025-01014-w

Reference

For the full scraped page text/schemas, read:

• references/source.md