Rein Agent Risk Scale: MCP examples
MCP serverEverything elseIndicative Agent Risk Rating (A-E) self-check for AI agents that spend money. Not a credit rating.
Use Rein Agent Risk Scale: MCP examples in Claude, ChatGPT or Ahel Desktop
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Also: Claude Code · Cursor · Codex
Then ask your AI: use Rein Agent Risk Scale: MCP examples
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Details
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Install Rein Agent Risk Scale: MCP examples
The server’s own address, for the clients that take one directly. Or connect ahel once and every client you use reads it from one address, with the account kept on ahel rather than in each client’s config.
Claude Code
claude mcp add --transport http --scope user rein-agent-risk-scale-mcp-exampl 'https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp'Run it once in your project, then open /mcp to approve any sign-in the server asks for.
Claude Desktop
https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcpAdd a custom connector in Settings, paste this address, and approve the sign-in.
Cursor
cursor://anysphere.cursor-deeplink/mcp/install?name=rein-agent-risk-scale-mcp-exampl&config=eyJ1cmwiOiJodHRwczovL3JlaW4tc2NhbGUubWFuZ293YXZlLWFkMTkxZDljLmVhc3R1cy5henVyZWNvbnRhaW5lcmFwcHMuaW8vbWNwIn0=Open the link and Cursor adds the server at that address.
ChatGPT
https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcpIn Settings, enable Developer mode, create an MCP app, and paste this address. Your plan and workspace must allow custom apps.
Codex
codex mcp add rein-agent-risk-scale-mcp-exampl --url 'https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp'Run it once, then sign in with codex mcp login rein-agent-risk-scale-mcp-exampl if the server asks for an account.
From the project's README
As published by reinlayer/rein-examples in README.md.
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_checkscores a deployment from structured answers;get_scalereturns the A-E scale 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.
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
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.
Advanced
- Delivery
- rein-agent-risk-scale MCP server → your ahel connector (mcp.ahel.ai) → your AI.
- Item type
- mcp-server
- Key
io-github-reinlayer-rein-agent-risk-scale- Source
- github.com/reinlayer/rein-examples
- Hosted endpoint
https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp