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AfterLaunch: the agentic growth marketing engine

by io.afterlaunch

get_visibility

Read-only

The measured AI visibility (GEO) results: how ChatGPT, Gemini, Perplexity and Google AI Overviews answer the tracked buyer questions, plus share of voice, cited sources, competitor-owned gaps, the trend, AI crawlability and the off-site reach fold. Every reading carries its own sample count, and the sampling block says what such a count licenses, so treat a change as indicative unless it says otherwise. Read after refresh_scan and before any AI visibility or GEO work. Read-only, free. Pass prompt_id to drill into one question.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
prompt_idNoDrill into one tracked question by its id (from the prompts list) for the engine answer text and citations.

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnlyHint and destructiveHint annotations, the description adds meaningful behavioral context: the sampling count's statistical implications ('treat a change as indicative unless it says otherwise'), the 'Read-only, free' note, and the sequencing dependency on refresh_scan. This is substantial value beyond annotations.

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?

The description is succinct yet dense, condensing a large amount of information into a few sentences. Every clause serves a purpose: defining the output scope, qualifying data reliability, directing workflow, and explaining the optional parameter. No fluff or redundancy.

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?

Despite lacking an output schema, the description enumerates all major output components (engine answers, share of voice, sources, gaps, trend, crawlability, off-site reach) and explains the sampling caveat. It also covers the single parameter and its effect. For a read-only, relatively simple tool, this is complete enough for an agent to set expectations.

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 description coverage is 100%, so the baseline is 3. The description echoes the schema's guidance ('Pass prompt_id to drill into one question') without adding new semantic detail beyond what the schema states, so no bonus is warranted.

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 clearly specifies the resource and action: 'The measured AI visibility (GEO) results' with detailed constituent elements (engine answers, share of voice, cited sources, gaps, trends, crawlability). It distinguishes from siblings like get_seo by focusing on AI visibility/GEO rather than traditional SEO or other analytics tools.

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?

Provides explicit workflow context: 'Read after refresh_scan and before any AI visibility or GEO work,' which tells when to use it. It also gives conditional usage via the prompt_id parameter ('Pass prompt_id to drill into one question'). However, it does not explicitly state alternatives or when not to use it, stopping short of a 5.

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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TDQS

A3.9/5.0
Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness3/5

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.

Resources