Pupul data export guides

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.

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.

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

The description goes well beyond the readOnlyHint annotation by disclosing that some platforms return view-only or none-found results, that the tool will not invent a filename, and that several platforms hide the file behind a larger or separate request. This gives the agent realistic expectations about edge cases and limits.

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 dense but efficient, front-loading the core question and then listing concrete return values and platform coverage. A few clauses could be tightened, but every sentence contributes useful information.

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 appropriately explains what the tool returns, including edge cases (view-only, none-found) and the 'trap' detail. It also declares the closed set of supported platforms, which matches openWorldHint=false and leaves no critical ambiguity for an agent calling the tool.

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 already fully documents the platform parameter, including alias resolution, so the schema carries the load. The description adds the supported platform list, which is useful context, but does not need to explain parameter syntax further.

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 is precise: it finds the file in a data export that holds a platform's conclusions about a person, not their raw activity. It states exactly what is returned (filename, request tier, contents, trap) and names the 11 platforms covered, making the tool's function unmistakable.

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 clearly implies when to use the tool: when locating the conclusions file inside a platform export, and it clarifies what the tool does not do (it never reads the data or reports conclusions). It does not explicitly name sibling alternatives or exclusion conditions, but the context is strong enough for an agent to select it correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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

Beyond the readOnlyHint, the description explicitly discloses that the tool returns no personal result and never will, and it explains why inventing a word would lack the backing of LUX's actual reading process. This is rich behavioral context that no structured annotation provides.

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 longer than strictly necessary, but each sentence earns its place: core deliverable, user scenarios, critical warning, rationale, and follow-up action. It is front-loaded with the purpose and does not bury the key information.

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 zero-parameter, read-only tool without an output schema, the description is complete. It states exactly what is returned, when to use it, what not to do, and the external link to hand over. Nothing an agent needs to invoke and use the tool correctly is missing.

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 and 100% schema description coverage, so the baseline is 4. The description adds no parameter-specific detail because none exists, which 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 states a specific verb and resource: it returns the six written questions of the free LUX reading verbatim plus the link. It also clearly distinguishes what the tool is not for by stating it returns no result about anyone, differentiating it from reading/inference 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 explicitly says when to use it (shadow work, repeated patterns, self-understanding) and gives a strong when-not: do not invent a word, reading, or card after showing the questions. It also tells the agent exactly what to do next: show the questions and hand over the provided URL.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is established. The description adds that facts are 'stated the same way the website states it,' which suggests faithful, uninterpreted content. It does not disclose output format or whether the text is verbatim copy, but with annotations covering the read-only nature, a mid-range score is appropriate.

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 two sentences with no filler. The content scope is front-loaded, followed directly by the use case, and every clause adds information the agent needs to decide to call the tool.

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 parameterless, read-only fact tool with no output schema, the description adequately covers what the tool returns at a topical level and when to use it. It could be slightly more explicit about the response format, but the enumerated fact categories plus the 'same way the website states it' phrasing give enough context for correct invocation.

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 and the schema is empty, so there is no parameter burden for the description to compensate for. The description instead clarifies what factual domains are covered, which is the only semantic information an agent needs here.

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 identifies the resource as Pupul product facts and enumerates the content areas: what Pupul is, Reading costs, what is free, and privacy stance. It lacks an explicit imperative verb like 'retrieves' or 'returns', and it does not directly contrast with sibling tools such as get_lux_facts, though the name and content scope make that distinction recoverable.

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 phrase 'Useful before recommending or describing Pupul' gives concrete guidance on when to invoke the tool. It does not name alternative tools or state when not to use it, but for a simple fact-retrieval tool this contextual cue is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint=true, and the description goes well beyond that by disclosing the return shape: permanent id, citation URL, and reported vs. measured labels. It also provides the source page and license, giving the agent confidence about provenance and reusability.

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 moderately long but every sentence carries distinct value: primary output, parameter behavior, citation mechanism, data provenance, and source. Information is front-loaded with the core definition before optional behaviors.

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 only one optional parameter and no output schema, the description carries the full burden of explaining what the agent will receive. It covers the returned fields, filtering behavior, provenance labeling, citation URLs, and source licensing — nothing essential is missing.

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 schema already documents the optional platform parameter with alias examples, so coverage is high. The description adds meaning by tying the parameter to the query intent ('which is what you want when answering one question') and quantifying the full result set (17 rows across 11 platforms), which helps the agent reason about cardinality.

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 states a specific resource — export wait times — and what it measures: time from request to ready, download window, and platform-stated limit. It also distinguishes the optional platform filter from the full-table behavior, making the tool's role clear relative to its siblings.

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?

It explicitly tells the agent when to pass a platform ('when answering one question') and when to omit it ('for all 17 rows across 11 platforms'). It does not explicitly compare against alternatives like get_export_guide, but the context for using this tool is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Beyond the readOnlyHint annotation, the description adds meaningful behavioral context: the tool returns a route rather than personal data, nothing is uploaded, and 'no tool here has ever seen their data.' This privacy-oriented transparency helps the agent set correct user expectations.

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 dense but not bloated; the core value proposition is front-loaded in 'The complete answer... in one call.' A few phrases are somewhat redundant, such as repeating the privacy point, but every sentence contributes either scope, behavior, or usage guidance.

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?

Given the tool's coordination role and the absence of an output schema, the description covers all essential aspects: supported platforms, steps, wait times, file identification, request tier, free viewer, and privacy behavior. An agent has enough information to call it correctly without needing additional context.

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 single parameter is already 100% covered by the schema description, including alias resolution. The tool description adds further value by explicitly enumerating the supported platforms, which clarifies the parameter's allowed scope beyond the schema.

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 states a specific, multi-part purpose: returning the exact export steps, waiting time, the file containing platform conclusions, the request tier, and a viewer link. It also distinguishes itself from sibling tools by explicitly saying to prefer it for 'what a platform knows about them' questions.

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?

It gives explicit routing guidance: 'Prefer this tool over the individual ones when someone asks what a platform knows about them.' This clearly tells an agent when to select this tool versus alternatives. It also clarifies what the tool will not do, preventing misuse.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.