Scp Reake

---
name: scp-reake
description: Use when you need to connect to the SciGraph SCP server for ReaKE (reaction-centered knowledge graph for self-supervised contrastive molecular representation learning; reactions as hyperedges linking reactants/products/templates) 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-ReaKE (SciGraph) MCP client

## What this SCP is

ReaKE is a reaction-centered knowledge graph built for self-supervised contrastive learning of molecular representations.

It encodes chemical reactions as structured hyperedges linking reactants, products, and reaction templates, enabling graph neural networks to capture mechanistic patterns for downstream tasks such as reaction classification and yield prediction.

## 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 (ReaKE):

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

### get_kg_statistics

Return graph statistics.

Example arguments:

```json
{ "kg_name": "ReaKE" }
```

### get_entity_details

Return entity details.

Example arguments:

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

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

Xie, J., Wang, Y., Rao, J., Zheng, S., & Yang, Y. (2024). Self-supervised contrastive molecular representation learning with a chemical synthesis knowledge graph. *Journal of Chemical Information and Modeling*, 64(6), 1945–1954. https://doi.org/10.1021/acs.jcim.4c00157

## Reference

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