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Track a question

track_prompt
Destructive

Add one customer question to the AI visibility (GEO) set AfterLaunch measures this product on. Free, spends nothing. Two honest consequences: changing the set makes the NEXT scheduled scan run at full depth rather than skipping ahead, and it re-baselines the week-over-week trend, because a comparison across two different question sets is not a real move. So add deliberately rather than churning the list. 10 to 200 characters, deduplicated, up to the included 50. Requires the 'config' scope and the Founder plan; on a free trial the questions stay readable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe customer question to track, as a person would ask an AI assistant, e.g. "What is the best invoicing tool for UK freelancers?".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / text / description
      Previous value: -"The buyer question to track, as a real person would ask an AI assistant, e.g. \"What is the best invoicing tool for UK freelancers?\"."New value: +"The customer question to track, as a person would ask an AI assistant, e.g. \"What is the best invoicing tool for UK freelancers?\"."
  2. Added

TDQS

A4.5/5.0
Behavior5/5

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

Goes well beyond the destructiveHint=true annotation by spelling out the two concrete consequences (next scan runs at full depth, week-over-week trend is re-baselined) plus cost, 'config' scope, Founder-plan requirement, and free-trial readability. This is exactly the side-effect disclosure a mutation tool needs.

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

Conciseness4/5

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

Purpose is front-loaded and every clause (cost, consequences, limits, permission requirements) carries information. It is dense and slightly long, but not padded.

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

Completeness4/5

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

For a one-parameter mutation with no output schema, it covers permissions, limits, and side effects thoroughly. It does not describe what is returned on success, which is a minor gap.

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

Parameters4/5

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

Schema coverage is 100% and already documents text's type and length bounds, so the baseline is 3. The description earns a point by adding constraints not in the schema: deduplication and the included-50 total limit.

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?

States a specific verb+resource: 'Add one customer question to the AI visibility (GEO) set'. This clearly distinguishes it from sibling untrack_prompt and list_tracked_prompts without needing to open any schema.

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

Usage Guidelines4/5

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

Gives real usage context: it is free, and the agent is told to 'add deliberately rather than churning the list' because of the re-baselining consequence. It does not explicitly name untrack_prompt as the removal alternative, but the when-to-use condition is clear.

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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