---
name: scp-clinicalkg-d533b7277791
description: Use when you need to connect to the SciGraph SCP server for ClinicalKG/CKG (Clinical Knowledge Graph database dump for building Neo4j; proteomics + clinical decision support) 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-ClinicalKG (SciGraph) MCP client

## What this SCP is

ClinicalKG refers to the **Clinical Knowledge Graph (CKG)** database dump for rapid construction of a Neo4j graph database.

CKG is an open-source platform integrating 9 ontologies, 26 biomedical databases, and experimental data to accelerate proteomics workflow analysis and clinical decision-making using graph representations and ML.

## 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 (ClinicalKG):

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

### get_kg_statistics

Return graph statistics.

Example arguments:

```json
{ "kg_name": "ClinicalKG" }
```

### get_entity_details

Return entity details.

Example arguments:

```json
{ "entity_identifier": "experiment_1", "kg_name": "ClinicalKG" }
```

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

Santos, A., Colaço, A. R., Nielsen, A. B., Niu, L., Geyer, P. E., Coscia, F., Albrechtsen, N. J. W., Mundt, F., Jensen, L. J., & Mann, M. (2020). Clinical knowledge graph integrates proteomics data into clinical decision-making. *bioRxiv*. https://doi.org/10.1101/2020.05.09.084897

## Reference

For the full scraped page text/schemas, read:
- `references/source.md`


---
Source attribution: zjunlp
Original project: https://github.com/zjunlp/Skills/blob/0a76d47c37fcb9badc654a872341a2185c45902e/skills/skill-collections/SciGraph-SCP-Skills/scp-clinicalkg/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 ZJUNLP

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: zjunlp
Original project: https://github.com/zjunlp/Skills/blob/0a76d47c37fcb9badc654a872341a2185c45902e/skills/skill-collections/SciGraph-SCP-Skills/scp-clinicalkg/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Education & Research, Healthcare & Life Sciences
Roles: Clinical Research, Data Scientist, Health Informatics, Researcher
Specialties: API, Backend
Tools: Neo4J, Python
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: aae33eb994b5fd0b5c2b95298a6c15d24e079dec3414ee08e8d0b077fa2ab497