Run an Inference Doctor simulation for access, role, endpoint, policy, and resource debugging.
AI agents invoke doctor_simulate to trigger actions in Tuning Engines - LLM Fine-Tuning. 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.
doctor_simulate triggers real processes with real consequences. An agent gone sideways doesn't fire it once — it starts dozens of builds, sends mass notifications, or burns through compute before anyone looks up.
Attacks that exploit this kind of access
Run an Inference Doctor simulation for access, role, endpoint, policy, and resource debugging. It is categorised as a Execute tool in the Tuning Engines - LLM Fine-Tuning MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Tuning Engines - LLM Fine-Tuning MCP server in PolicyLayer and add a rule for doctor_simulate: 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 Tuning Engines - LLM Fine-Tuning. Nothing to install.
doctor_simulate 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 doctor_simulate 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 doctor_simulate. 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.
doctor_simulate is provided by the Tuning Engines - LLM Fine-Tuning MCP server (tuningengines-cli). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.