diagnose_schedule
AI agents use diagnose_schedule to create or update resources in SchedulerRX · Constraint Debugger Agent — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your SchedulerRX · Constraint Debugger Agent environment.
An AI agent can call diagnose_schedule faster than any human can review — one bad instruction and it creates or modifies resources in SchedulerRX · Constraint Debugger Agent by the hundred, each call as confident as the last.
Attacks that exploit this kind of access
diagnose_schedule. It is categorised as a Write tool in the SchedulerRX · Constraint Debugger Agent MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the SchedulerRX · Constraint Debugger Agent MCP server in PolicyLayer and add a rule for diagnose_schedule: 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 SchedulerRX · Constraint Debugger Agent. Nothing to install.
diagnose_schedule is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the diagnose_schedule 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 diagnose_schedule. 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.
diagnose_schedule is provided by the SchedulerRX · Constraint Debugger Agent MCP server (tpinzzz/schedulerrx-constraint-agent). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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