get_lux_factsWhat the LUX reading is, what the word means, what happens after, what it costs, and the things it explicitly is not (not a personality test, not a type, not astrology, not therapy, no clinical claims). Read this before describing or recommending LUX so the description matches what the product actually does.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, covering the basic safety profile. The description adds useful context about the tool's purpose and, importantly, what the content is not (not a personality test, not therapy, no clinical claims), which helps prevent misuse of the returned facts.
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 one dense sentence that front-loads the content coverage and closes with a clear action directive. Every phrase earns its place, and there is no redundancy.
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, parameterless, read-only facts tool, the description is complete: it names the content scope, exclusions, and the recommended usage context. No output schema exists, but the description gives enough context for an agent to call the tool and use the result appropriately.
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 takes zero parameters, so there are no parameter semantics to clarify. The baseline of 4 applies, and the description appropriately focuses on content rather than inputs.
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 specifies exactly what the tool covers: what the LUX reading is, the meaning of the word, what happens after, cost, and explicit exclusions. This distinguishes it from sibling tools like get_lux_reading_questions, which concern questions rather than product facts.
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 an explicit usage instruction: read this before describing or recommending LUX. This is clear contextual guidance for when the tool should be invoked, though it does not explicitly name alternatives or when-not-to-use conditions.
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.