get_export_guideThe exact steps to request a personal data export from a platform, with the reported wait time, download window, file size and what is inside the file. Same content as the guide at https://pupulcorp.com/data/. Platforms: amazon, chatgpt, claude, google, grok, instagram, linkedin, netflix, spotify, tiktok, x.
| Name | Required | Description | Default |
|---|---|---|---|
| platform | Yes | Which platform to get the export guide for. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as read-only, and the description does not contradict that. It adds useful behavioral context by specifying what the guide contains (steps, reported wait time, download window, file size, and inside-file details) and that the content is identical to the referenced guide, helping the agent set expectations about the response.
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 concise and front-loaded with the core value statement, followed by a useful URL reference and platform list. The platform enumeration is somewhat redundant with the schema but is still scannable and does not introduce unnecessary fluff.
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 simple one-parameter read-only tool, the description provides enough detail: the guide's contents, the platform list, and a reference URL. It does not explicitly describe response formatting, but the absence of an output schema is partly mitigated by the clear statement of what the guide contains.
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?
Schema coverage is 100%, and the single platform parameter is already fully documented with an enum and description. The description repeats the platform list, which is redundant with the schema rather than adding new semantic meaning, so the baseline score of 3 is appropriate.
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 names a specific verb and resource: it provides the exact steps to request a personal data export from a platform. It also enumerates the included details (wait time, download window, file size, contents) and lists the supported platforms, making the tool's purpose unmistakable and distinct from siblings like get_wait_times.
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 intended use is clear from the purpose: call this when someone needs the steps to request a data export from a listed platform. However, it gives no explicit when-to-use versus alternatives, nor does it mention exclusions such as 'use get_wait_times for general wait time information', leaving differentiation to inference.
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.