Rein Agent Risk Scale

npx add-mcp 'https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp'

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-E scale, 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_check scores a deployment from structured answers;
• get_scale returns the A-E scale and what each grade means;
• explain_grade says what a grade requires.

Scoring is fixed, published rules with no language model, so the same answers always give the same grade.

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. mcp 2.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 E (unverified) 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.