Rein Agent Risk Scale
Xếp hạng Rủi ro Chỉ định của Tác nhân (A–E) tự kiểm tra cho các tác nhân AI có thể chi tiền, dựa trên trách nhiệm giải trình, giới hạn chi tiêu, giới hạn người thụ hưởng, công tắc dừng, leo thang con người, giám sát, lịch sử và sự cố. Tự khai báo và chưa được xác minh; không phải là xếp hạng tín dụng.
Máy chủ MCP được lưu trữ
npx add-mcp 'https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp'Cài vào Claude Code, Codex, Cursor và nhiều công cụ khác
Tài liệu
Rein Agent Risk Scale: MCP examples
Copy-paste examples that connect an AI agent to the Rein Agent Risk Scale self-check over MCP. The self-check gives an agent that can spend money an indicative Agent Risk Rating on an A–D scale (NR = not rated), from the controls around it: who is accountable, spend caps and where they are enforced, payee limits, a stop switch and who holds it, human escalation, monitoring, history and incidents.
MCP endpoint (streamable HTTP, no sign-in):
https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp
The endpoint has three tools:
self_checkscores a deployment from structured answers;get_scalereturns the A–D scale (NR = not rated) and what each grade means;explain_gradesays what a grade requires.
Scoring is fixed, published rules with no language model, so the same answers always give the same grade (methodology 0.3.1). Harness memory counts in your favour within your grade's band; it never moves the letter.
Self-declared and unverified; not a credit rating.
Examples
Each example is one file of under 40 lines.
| file | framework | install (tested versions) |
|---|---|---|
examples/plain_mcp.py | the mcp Python SDK, no LLM | pip install "mcp==2.3.0" |
examples/openai_agents.py | OpenAI Agents SDK | pip install "openai-agents==0.23.1" |
examples/langgraph_agent.py | LangChain / LangGraph with the MCP adapters | pip install "langchain==1.4.3" "langchain-openai==1.6.7" "langchain-mcp-adapters==0.3.2" |
examples/crewai_agent.py | CrewAI | pip install "crewai==1.15.23" "crewai-tools[mcp]==1.15.23" |
The three agent examples use OpenAI by default (export OPENAI_API_KEY=...). Pass any model your framework accepts to main() to use another.
- Tested: every example was run against the live endpoint on 7 October 2026 at the versions above.
- Watched weekly: a check runs them against the latest framework releases, because releases rename things.
mcp2.x renamed its HTTP client, and it now yields two streams instead of three.
Use it from any MCP client
{
"mcpServers": {
"rein-agent-risk-scale": {
"type": "http",
"url": "https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp"
}
}
}
There are docs written for agents at /skill.md and /llms.txt.
What a self-check is, and is not
- Indicative. The grade comes from your own answers. It is self-declared and unverified, so the rated grade stays
NR(not rated) until the controls are verified. - Explained. The response lists the findings that most explain the grade and the steps that would raise it.
- Private by default. Answers and grades are stored under a pseudonymised record. A handle is published only if you set
publish_opt_in: true. Never send secrets or personal data. - Not a credit rating, not investment advice, and not an offer of credit.
License
MIT. See LICENSE.