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Indicative Agent Risk Rating (A-E) self-check for AI agents that spend money. Not a credit rating.
About
Indicative Agent Risk Rating (A-E) self-check for AI agents that spend money. Not a credit rating.
Remote endpoints: streamable-http: https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp
Security Report
Valid MCP server (1 strong, 0 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
3 tools verified · Open access · No issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Found in Source Code
Found by scanning the linked source code. This listing connects to a hosted endpoint, so none of this runs on your machine: it describes what the server software does where it is hosted.
How to Connect
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-reinlayer-rein-agent-risk-scale": {
"url": "https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
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_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.
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