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MentionedOn Discovery Evidence Tools

Build an AI Visibility Action Plan

build_visibility_action_plan
Read-only

Prioritize fixes from caller-supplied findings using a deterministic rubric. This tool does not edit a website, contact third parties, persist data, or promise rankings or mentions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYesThe outcome used to break ties in the deterministic priority rubric.
findingsYesCaller-supplied findings. Present findings are acknowledged but do not create actions.
businessNameYes
effortBudgetYesControls how many actions are returned and how they are phased.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYes
scopeYes
actionsYes
goalLabelYes
limitationsYes
businessNameYes
effortBudgetYes
omittedActionCountYes
excludedPresentSignalsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only and non-destructive behavior; the description adds substantial context by stating it does not edit websites, contact third parties, persist data, or promise rankings or mentions. Disclosing the deterministic rubric also helps the agent understand repeatability.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the primary action and resource, followed by concise non-behavior exclusions. Every sentence earns its place with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only planning tool with a rich input schema and an output schema, the description sufficiently covers purpose, source of input, and behavioral boundaries. The deterministic-rubric note explains how prioritization works without requiring schema restatement.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is high at 75%, with enums and descriptions for most parameters. The description adds little beyond what the schema already provides, though 'caller-supplied findings' reinforces that findings come from the caller rather than being discovered by the tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Prioritize') and resource ('fixes from caller-supplied findings'), clearly distinguishing this from the sibling analysis/summarization tools. It also names what it does not do, which sharpens the boundary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this tool should be used when the caller already has findings and wants prioritized fixes, but it never explicitly contrasts this with analyze_discovery_readiness or summarize_scan_evidence. The exclusions are behavioral boundaries, not usage routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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