active_learning.execute_retraining
Queue the exact training run created by an artifact-bound active-learning plan.
This record as markdown: /tools/visionmcp/active-learning.execute-retraining.md
What active_learning.execute_retraining does on Visionmcp
AI agents invoke active_learning.execute_retraining to trigger actions in Visionmcp. 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.
Why active_learning.execute_retraining is rated High
This tool initiates a training pipeline execution. While not destructive (the plan was pre-created by active_learning.plan_retraining), executing arbitrary training runs can consume significant computational resources, modify model state, and produce unpredictable outcomes depending on the training data and hyperparameters. The blast radius is high if an agent queues unvetted or malicious retraining jobs.
From the tool's definition Tool name includes 'execute' and description states it 'Queue[s] the exact training run' — this triggers an external machine-learning operation whose effects (model updates, resource consumption, training artifacts) depend on the plan parameters and cannot be…
Attacks that exploit this kind of access
The rule that runs active_learning.execute_retraining safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Visionmcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For active_learning.execute_retraining, this is the rule to start with:
active_learning.execute_retraining 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Visionmcp, apply this rule, and every active_learning.execute_retraining call is checked against it from then on.
Questions about active_learning.execute_retraining
Queue the exact training run created by an artifact-bound active-learning plan. It is categorised as a Execute tool in the Visionmcp MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Vision MCP server in PolicyLayer and add a rule for active_learning.execute_retraining: 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 Visionmcp. Nothing to install.
active_learning.execute_retraining 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 active_learning.execute_retraining 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 active_learning.execute_retraining. 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.
active_learning.execute_retraining is provided by the Vision MCP server (joshuahickscorp/visionmcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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