active_learning.plan_retraining
Create a correction dataset and offline training run against a fixed benchmark.
This record as markdown: /tools/visionmcp/active-learning.plan-retraining.md
What active_learning.plan_retraining does on Visionmcp
AI agents invoke active_learning.plan_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.plan_retraining is rated High
This tool triggers an external operation (model retraining) whose effects are significant and depend on arguments (the correction dataset, benchmark, and training parameters). While it doesn't delete data (not Destructive) or move money (not Financial), it executes a complex computational process that modifies ML model state.
From the tool's definition Tool performs 'offline training run' which executes machine learning operations that modify model state and behavior.
Attacks that exploit this kind of access
The rule that runs active_learning.plan_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.plan_retraining, this is the rule to start with:
active_learning.plan_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.plan_retraining call is checked against it from then on.
Questions about active_learning.plan_retraining
Create a correction dataset and offline training run against a fixed benchmark. 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.plan_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.plan_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.plan_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.plan_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.plan_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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