---

name: scp-pspp description: Use when you need to connect to the SciGraph SCP server for PSPP / PSPP-KG (process-structure-property-performance materials design knowledge graph with influence relations extracted from literature) 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-PSPP (SciGraph) MCP client

What this SCP is

PSPP-KG is a materials-design knowledge graph based on the process-structure-property-performance (PSPP) reciprocity paradigm.

It represents scientific concepts (e.g., annealing, grain size, strength) as nodes and their influence relationships as edges, reflecting the PSPP assumption that:

• processes influence structures
• structures influence properties (and performance)

The graph is built from scientific literature using weakly supervised learning and supports visualization of design charts that show paths from processes to desired properties.

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 (PSPP):

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

get_kg_statistics

Return graph statistics.

Example arguments:

{ "kg_name": "PSPP" }

get_entity_details

Return entity details.

Example arguments:

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

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

Onishi, T., Kadohira, T., & Watanabe, I. (2018). Relation extraction with weakly supervised learning based on process-structure-property-performance reciprocity. Science and Technology of Advanced Materials, 19(1), 649–659. https://doi.org/10.1080/14686996.2018.1500852

Reference

For the full scraped page text, read:

• references/source.md