The buyer questions AfterLaunch tracks: the AI visibility (GEO) question set every measurement is taken against. Returns prompts in tracking order, cap (a hard 15) and used, plus curated: false means AfterLaunch generated them and any can be replaced. Read before track_prompt or untrack_prompt so you never duplicate a question or guess at the room left. Read-only, free, on every plan.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds substantial context: prompts returned in tracking order, a hard cap of 15, a 'used' count, the meaning of the 'curated' flag (false means auto-generated and replaceable), and that it's free on every plan. This exceeds the annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is packed with useful information in three sentences, but the first sentence is a slightly awkward fragment ('The buyer questions AfterLaunch tracks...') that could be more grammatically fluid. Every sentence earns its place, though.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description compensates by explaining key return fields (prompts, tracking order, cap, used, curated) and when to use it. However, the meaning of 'used' is not fully explicit (likely a count, but not stated), and the structure of each prompt (e.g., text vs ID) is unaddressed, leaving minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters (empty schema), so there are no input semantics to clarify. The description doesn't need to compensate for parameter gaps, and the 0-param baseline of 4 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the tracked buyer questions for AfterLaunch's AI visibility (GEO) measurement, with a specific verb ('Returns') and details on the data structure (order, cap, used, curated). It distinguishes itself from mutating siblings like track_prompt and untrack_prompt.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs to read this tool before track_prompt or untrack_prompt to avoid duplicate questions and to know remaining capacity. This provides concrete when-to-use guidance and rationale relative to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools follow a clear action+resource pattern (get_, list_, set_, ship_, etc.) with distinct purposes. A few boundaries could cause confusion, such as get_move vs get_output (both return drafts) and ship_move vs set_output_status (both mark things shipped), though descriptions mitigate this sufficiently.
The overwhelming majority use consistent snake_case verb_noun naming (list_feed, ship_move, get_visibility). The only outlier is 'whoami', a common convention but not verb_noun; otherwise the pattern is highly predictable.
At 41 tools, the surface is well over the 25-tool threshold for 'too many'. While the domain is broad, this volume places significant cognitive load on agents and suggests some consolidation or modularization could help.
Core workflows for moves and outputs are covered (create/list/get/update/ship/skip/undo). However, the tool descriptions reference 'approve_output' as the publishing action, yet that tool is absent from the list, leaving a dead end for channel distribution. There are also no tools for editing competitors or managing Memory pages beyond recording insights.