active_learning.compare
Recompute non-regression from two stored fixed-benchmark evaluations.
This record as markdown: /tools/visionmcp/active-learning.compare.md
What active_learning.compare does on Visionmcp
AI agents call active_learning.compare to retrieve information from Visionmcp without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why active_learning.compare is rated Low
The tool reads and recomputes a comparison between two existing stored evaluations. It does not modify, delete, or execute any external operations — it simply derives a non-regression metric from already-stored data, which is a read/query operation.
From the tool's definition Recompute non-regression from two stored fixed-benchmark evaluations
Attacks that exploit this kind of access
The rule that runs active_learning.compare 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.compare, this is the rule to start with:
active_learning.compare is read-only, so it stays allowed. 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.compare call is checked against it from then on.
Questions about active_learning.compare
Recompute non-regression from two stored fixed-benchmark evaluations. It is categorised as a Read tool in the Visionmcp MCP Server, which means it retrieves data without modifying state.
Register the Vision MCP server in PolicyLayer and add a rule for active_learning.compare: 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.compare is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the active_learning.compare 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.compare. 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.compare 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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