active_learning.record_corrections
Bind named human corrections to requested predictions.
This record as markdown: /tools/visionmcp/active-learning.record-corrections.md
What active_learning.record_corrections does on Visionmcp
AI agents use active_learning.record_corrections to create or update resources in Visionmcp, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Visionmcp environment.
Why active_learning.record_corrections is rated Medium
This tool writes/associates human correction data to existing predictions. It modifies stored prediction records by attaching corrections, which is a reversible write operation. It does not execute code, delete data, or involve financial transactions.
From the tool's definition Bind named human corrections to requested predictions
Attacks that exploit this kind of access
The rule that runs active_learning.record_corrections 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.record_corrections, this is the rule to start with:
active_learning.record_corrections stays usable, but capped: an agent stuck in a loop can't make hundreds of changes 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.record_corrections call is checked against it from then on.
Questions about active_learning.record_corrections
Bind named human corrections to requested predictions. It is categorised as a Write tool in the Visionmcp MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Vision MCP server in PolicyLayer and add a rule for active_learning.record_corrections: 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.record_corrections 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 active_learning.record_corrections 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.record_corrections. 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.record_corrections 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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