validate_feedback
Parse unstructured feedback (user notes, review comments, bug reports) into candidate tickets, each with a suggested type (feature/bug), product, and priority from deterministic keyword heuristics only — no model calls. DRY-RUN BY DEFAULT (apply:false, the default): returns the structured candida...
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What validate_feedback does on FeatureBoard
AI agents use validate_feedback to create or update resources in FeatureBoard, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your FeatureBoard environment.
Why validate_feedback is rated Medium
The tool's default mode is a read/parse operation (dry-run), but its full capability includes bulk-creating tickets when apply:true is passed. Since it can create data (Write), and creation is reversible (tickets can be deleted), Write is the appropriate category. It does not execute code, delete data, or involve finances.
From the tool's definition When called with apply:true, the tool 'bulk-create the candidates' — creating tickets from parsed feedback. Default is dry-run (apply:false) which creates nothing, but the tool's ultimate purpose and effect is creation of records.
Attacks that exploit this kind of access
The rule that runs validate_feedback safely
PolicyLayer is an MCP gateway: it sits between your AI agents and FeatureBoard, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For validate_feedback, this is the rule to start with:
validate_feedback 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 FeatureBoard, apply this rule, and every validate_feedback call is checked against it from then on.
Questions about validate_feedback
Parse unstructured feedback (user notes, review comments, bug reports) into candidate tickets, each with a suggested type (feature/bug), product, and priority from deterministic keyword heuristics only — no model calls. DRY-RUN BY DEFAULT (apply:false, the default): returns the structured candidate list for you to review/edit; creates NOTHING. Always dry-run first. When ready, call again with apply:true to bulk-create the candidates (optionally pass back an edited. It is categorised as a Write tool in the FeatureBoard MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the FeatureBoard MCP server in PolicyLayer and add a rule for validate_feedback: 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 FeatureBoard. Nothing to install.
validate_feedback 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 validate_feedback 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 validate_feedback. 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.
validate_feedback is provided by the FeatureBoard MCP server (https://github.com/valentil/featureboard-mcp/releases/download/v0.7/featureboard.plugin). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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