dataset.train_feature_model
Plan and queue offline feature-model training; no weights are downloaded.
This record as markdown: /tools/visionmcp/dataset.train-feature-model.md
What dataset.train_feature_model does on Visionmcp
AI agents invoke dataset.train_feature_model 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 dataset.train_feature_model is rated High
The tool queues and initiates a training job (an external computational operation), which falls under Execute. While no weights are downloaded, it still triggers an offline process with potentially significant resource consumption. Severity is medium since misuse could waste compute resources but doesn't directly destroy data or move money.
From the tool's definition "Plan and queue offline feature-model training" — this triggers an external training operation
Attacks that exploit this kind of access
The rule that runs dataset.train_feature_model 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 dataset.train_feature_model, this is the rule to start with:
dataset.train_feature_model 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 dataset.train_feature_model call is checked against it from then on.
Questions about dataset.train_feature_model
Plan and queue offline feature-model training; no weights are downloaded. 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 dataset.train_feature_model: 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.
dataset.train_feature_model 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 dataset.train_feature_model 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 dataset.train_feature_model. 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.
dataset.train_feature_model 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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