get_count_linkLink to the free in-browser viewer that opens a platform’s data export locally and shows the real numbers in about ten seconds. No account, no upload: the file never leaves the person’s device. Platforms: chatgpt, claude, google-takeout, grok, instagram, spotify, tiktok, x, youtube.
| Name | Required | Description | Default |
|---|---|---|---|
| platform | Yes | Which platform’s Count viewer to link. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only, and the description builds on that by explaining privacy-relevant behavior: no account is needed, no upload occurs, and the file never leaves the device. It also sets expectations around speed (about ten seconds), adding value beyond the structured annotations.
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?
Three short, focused sentences with no filler. The core purpose and main behavioral facts are front-loaded, and the platform list is the only necessary enumeration.
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?
For a tool with one required enum parameter, no output schema, and read-only annotations, the description is complete. It covers what the link is for, the supported platforms, the privacy behavior, and the expected speed, so an agent has everything needed to select and invoke it correctly.
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 input schema covers the single parameter fully, including the enum values and a description of what the platform parameter means. The tool description repeats the platform list but adds no additional parameter syntax or value-level guidance, so the schema already does the heavy lifting.
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 begins with a specific verb and resource: getting a link to the free in-browser Count viewer. It clearly identifies what the viewer does—opens a platform's data export locally and shows real numbers—and lists all supported platforms, making it easy to distinguish from sibling tools.
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?
The description gives clear context for when to use the tool: when the user wants a link to the free viewer for a platform's data export. It adds useful constraints like no account, no upload, and local-only processing, but it does not explicitly name sibling alternatives or state when not to use it, so it stops 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.
Add one secure layer between your agents and this server.
Each get_* tool targets a distinct deliverable—viewer links, export steps, inference file, wait times, or product facts—and the aggregate how_to_see_what_a_platform_knows explicitly positions itself as the router for whole-platform questions. The only mild overlap is that get_export_guide and get_wait_times both mention wait times, but their primary outputs are clearly different.
Seven tools follow a clean get_<object> pattern, making the target of each call predictable. how_to_see_what_a_platform_knows breaks the pattern as a sentence-style name, but it is a deliberate aggregate entry point rather than a sign of inconsistency.
Eight tools is a well-scoped set for an informational guide server: individual getters for specific facts, plus one combined entry point. No tool feels redundant or purely decorative.
The server covers the full question flow around data exports—request steps, wait times, inference files, and a local viewer—across the listed platforms, and adds LUX/Pupul factual tools for adjacent product questions. There are no obvious dead ends; the aggregate tool routes to individual tools where needed.