skills/skill-collections/SciGraph-SCP-Skills/scp-mekg/SKILL.md
Use when you need to connect to the SciGraph SCP server for MEKG (Materials Experiment Knowledge Graph capturing provenance of synthesis/processing/characterization/performance experiments) 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.
npx skillsauth add zjunlp/Skills scp-mekgInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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MEKG (Materials Experiment Knowledge Graph) encodes complete provenance for materials science experiments, including synthesis, processing, characterization, and performance analysis.
It models hierarchical relationships among material samples, experimental processes, and derived data to support querying and knowledge discovery in high-throughput experimental materials science.
https://scp.intern-ai.org.cn/api/v1/mcp/37/SciGraphSCP-HUB-API-KEY: {API-KEY}pip install mcp
{
"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}"
}
}
}
}
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 (MEKG):
{
"cypher": "MATCH (e:Experiment:MEKG) RETURN e.id as experiment_id",
"kg_name": "MEKG",
"limit": 5
}
Return graph statistics.
Example arguments:
{ "kg_name": "MEKG" }
Return entity details.
Example arguments:
{ "entity_identifier": "experiment_1", "kg_name": "MEKG" }
Return the full workflow of an experiment.
Example arguments:
{ "experiment_id": "experiment_1" }
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 MEKG
result = await session.call_tool(
"get_kg_statistics",
arguments={"kg_name": "MEKG"},
)
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())
Statt, M. J., Rohr, B. A., Guevarra, D., Breeden, J., Suram, S. K., & Gregoire, J. M. (2023). The materials experiment knowledge graph. Digital Discovery, 2(4), 909–914. https://doi.org/10.1039/D3DD00067B
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