Use when you need to connect to the SciGraph SCP server for Theoria (curated theoretical physics knowledge graph with JSON entries containing equations, derivations, assumptions, references, and verification code) 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
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---
name: scp-theoria
description: Use when you need to connect to the SciGraph SCP server for Theoria (curated theoretical physics knowledge graph with JSON entries containing equations, derivations, assumptions, references, and verification code) 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-Theoria (SciGraph) MCP client
## What this SCP is
Theoria is a curated theoretical physics knowledge graph derived from structured JSON entries. Each entry represents a theoretical result and includes equations, definitions, derivation steps, assumptions, references, contributors, and programmatic verification code.
## 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 (Theoria):
```json
{
"cypher": "MATCH (e:Experiment:Theoria) RETURN e.id as experiment_id",
"kg_name": "Theoria",
"limit": 5
}
```
### get_kg_statistics
Return graph statistics.
Example arguments:
```json
{ "kg_name": "Theoria" }
```
### get_entity_details
Return entity details.
Example arguments:
```json
{ "entity_identifier": "experiment_1", "kg_name": "Theoria" }
```
### 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 Theoria
result = await session.call_tool(
"get_kg_statistics",
arguments={"kg_name": "Theoria"},
)
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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