Report the outcome of a decision for the Boardroom learning system. Records what happened, whether the original recommendation was followed, and what was learned. Feeds the Knowledge Flywheel and updates the Trust Oracle if an entity is specified. Returns a warning if the outcome could not be per...
AI agents use report_outcome to create or update resources in Boardroom — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Boardroom environment.
This tool writes outcome data to a persistent store and updates trust scores for entities. It creates/modifies records (outcome logs, trust scores) in a reversible fashion — there's no indication of deletion or irreversible destruction.
From the tool's definition 'Records what happened, whether the original recommendation was followed, and what was learned. Feeds the Knowledge Flywheel and updates the Trust Oracle if an entity is specified.'
Attacks that exploit this kind of access
Report the outcome of a decision for the Boardroom learning system. Records what happened, whether the original recommendation was followed, and what was learned. Feeds the Knowledge Flywheel and updates the Trust Oracle if an entity is specified. Returns a warning if the outcome could not be persisted to disk. It is categorised as a Write tool in the Boardroom MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Boardroom MCP server in PolicyLayer and add a rule for report_outcome: 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 Boardroom. Nothing to install.
report_outcome 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 report_outcome 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 report_outcome. 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.
report_outcome is provided by the Boardroom MCP server (randysalars/boardroom-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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