Performs the login flow (and optional 2FA verify) and returns the raw server responses
AI agents invoke inspect_login to trigger actions in Rest Api. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool actively executes an authentication flow against an external server, triggering real network requests and potentially establishing authenticated sessions. It is not merely reading data — it performs actions with side effects (session creation, token issuance, 2FA verification).
From the tool's definition Performs the login flow (and optional 2FA verify) and returns the raw server responses
Documented attack patterns abuse exactly the kind of access inspect_login gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Rest Api, and nothing reaches the server without passing your rules. This is the rule we recommend for inspect_login:
{
"version": "1",
"default": "deny",
"tools": {
"inspect_login": {
"limits": [
{
"counter": "inspect_login_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}inspect_login stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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Performs the login flow (and optional 2FA verify) and returns the raw server responses. It is categorised as a Execute tool in the Rest Api MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Rest Api MCP server in PolicyLayer and add a rule for inspect_login: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Rest Api. Nothing to install.
inspect_login is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the inspect_login rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for inspect_login. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
inspect_login is provided by the Rest Api MCP server (muhammed-abdelghany/rest_api_mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Rest Api, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
5 Rest Api tools catalogued and risk-classified — across an index of 43,000+ MCP servers.