Use when you need to connect to the SciGraph SCP server for QUDT (quantities/units/dimensions/systems/prefixes/constants/datatypes ontology; based on QUDT-all-in-one-OWL.ttl) 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.
Scanned 5/29/2026
Install via CLI
openskills install zjunlp/Skills---
name: scp-qudt
description: Use when you need to connect to the SciGraph SCP server for QUDT (quantities/units/dimensions/systems/prefixes/constants/datatypes ontology; based on QUDT-all-in-one-OWL.ttl) 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-QUDT (SciGraph) MCP client
## What this SCP is
QUDT is an ontology/vocabulary knowledge graph for quantities, units, dimensions, systems, prefixes, constants, and datatypes. The packaged version described on the page is based on the official `QUDT-all-in-one-OWL.ttl` distribution.
## 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
```bash
pip install mcp
```
## Configure (MCP config JSON)
```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 (QUDT):
```json
{
"cypher": "MATCH (e:Experiment:QUDT) RETURN e.id as experiment_id",
"kg_name": "QUDT",
"limit": 5
}
```
### get_kg_statistics
Return graph statistics.
Example arguments:
```json
{ "kg_name": "QUDT" }
```
### get_entity_details
Return entity details.
Example arguments:
```json
{ "entity_identifier": "experiment_1", "kg_name": "QUDT" }
```
### get_experiment_workflow
Return the full workflow of an experiment.
Example arguments:
```json
{ "experiment_id": "experiment_1" }
```
## Python example (streamable HTTP)
```python
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 QUDT
result = await session.call_tool(
"get_kg_statistics",
arguments={"kg_name": "QUDT"},
)
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())
```
## Reference
For the full scraped page text, read:
- `references/source.md`
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