Rein Agent Risk Scale

io.github.reinlayerv0.1.0更新于 Oct 7, 2026

Indicative Agent Risk Rating (A-E) self-check for AI agents that spend money. Not a credit rating.

已验证Streamable HTTP可网页运行AI & ML Security & Monitoring Finance

概览

AI 生成的概览

让助手对可花钱的 AI 代理进行自报的 A-E 风险自检，并解释评级含义。

功能: 该远程 MCP 端点提供三个工具：self_check 根据结构化回答为部署打分，get_scale 返回 A-E 等级及各等级含义，explain_grade 说明某个等级需要满足什么。评分使用固定的公开规则而非语言模型，因此相同回答总是得到相同等级。响应会列出最能解释该等级的发现以及可提升等级的措施。
适用场景: 当你希望助手评估或记录可花钱代理周边的控制措施时使用，例如问责人、支出上限、收款方限额、停止开关、人工升级、监控和事件历史。它是自报的指示性检查，不是信用评级，也不是投资建议。
运行要求: 远程 streamable HTTP MCP 端点；未声明登录、API 密钥或环境变量。任何 MCP 客户端都可连接该端点地址。README 中的示例脚本使用 Python，代理示例还需要 OpenAI API 密钥。

安装前请注意

等级为自报且未经核实，因此在控制措施被核实前一直为 E（未核实）。回答和等级以假名化记录存储，只有设置 publish_opt_in 为 true 时才会公布标识。切勿发送机密或个人数据。

安装

在 SourceWeft 中

打开控制台中的 Rein Agent Risk Scale，将其添加到工作区。
为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。远程服务在工作区中配置后即可从网页运行时运行。

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "rein-agent-risk-scale": {
      "type": "http",
      "url": "https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp"
    }
  }
}

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):

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

json

{  "mcpServers": {    "rein-agent-risk-scale": {      "type": "http",      "url": "https://rein-scale.mangowave-ad191d9c.eastus.azurecontainerapps.io/mcp"    }  }}

{  "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.

来源：README.md，提交 536abb4

工具

工具元数据尚未被收录。

版本历史

v0.1.0最新Oct 7, 2026