puerto-rico

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

With no annotations provided, the description carries the full burden of disclosure. It openly states that results include the date of last human verification, a key behavioral trait, and that the directory is hand-verified. It also implies a read-only search operation via 'Busca'. It does not mention rate limits or error behavior, but for a straightforward search tool the coverage is solid.

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 wasted words. The first sentence states the action and scope; the second explains the unique return value and why it matters. It's front-loaded and every sentence earns its place.

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?

Since there is no output schema, the description correctly explains the return values (name, phone, address, verification date). It also provides geographic context and the directory's curation method. It does not detail pagination or parameter effects on output, but for a simple search tool with three parameters, this is sufficiently complete.

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 describes all three parameters with 100% coverage, so the baseline is 3. The description adds the context that results include a verification date, but it does not provide additional parameter-level semantics beyond the schema. Schema does the heavy lifting, so a 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 uses a specific verb ('Busca') and identifies the exact resource: verified local businesses and services in Puerto Rico, with a geographic focus on Cabo Rojo and the west. It clearly distinguishes itself from sibling tools (recalls, citations, local demand, health deserts, events) by topic and by its unique human-verification date output.

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 implies when to use the tool: when you need local business info from a hand-verified directory, especially for Puerto Rican towns, and it contrasts itself with Google Places. However, it does not explicitly state when not to use it or reference alternatives among the sibling tools, leaving the guidance implied rather than explicit.

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?

With no annotations, the description carries the full burden of behavioral disclosure. It adds useful context beyond the schema, such as data being updated daily, coverage area, and that the reason is provided in Spanish when it exists. This gives the agent a good sense of what to expect, though it does not explicitly state output structure or behavior when no recall is found.

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—two sentences. The first sentence states the core purpose, and the second adds relevant context (data update and coverage). Every sentence contributes meaning, with no redundancy or unnecessary detail.

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 simplicity (one parameter, no output schema, no annotations), the description is complete. It explains what the tool verifies, the output characteristic (reason in Spanish when it exists), data freshness, and coverage. This is sufficient for an agent to select and invoke the tool 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?

Schema description coverage is 100% with the parameter 'medicamento' having an example ('metformin'). The tool description adds no additional parameter-level semantics beyond what is already in the schema, so the baseline 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 clearly states the tool's function with a specific verb ('Verifica') and resource ('medicamento o producto tiene un recall activo de la FDA'). It also provides scope (Estados Unidos y Puerto Rico) and is clearly distinct from sibling tools, which focus on unrelated topics like business search and local demand.

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 provides clear usage context by stating the data source (FDA), update frequency (diaria), and geographic coverage (Estados Unidos y Puerto Rico). It does not explicitly mention when not to use it or name alternative tools, but the context is sufficient for a simple lookup tool.

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?

With no annotations, the description carries the full behavioral burden. It discloses that the returned citation explicitly indicates whether data was confirmed by a person or merely imported, and warns that imported is not verified. This is valuable beyond the raw schema.

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, front-loaded with the core function and ending with a clear usage statement. Every sentence adds value without 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 one-parameter tool with no output schema, the description covers the essential elements of the citation and the verification nuance. It does not mention error conditions or exact output formatting, but it is sufficient for the tool's simplicity.

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 fully described in the schema ('Nombre o slug del negocio o ficha'), and the description's mention of 'negocio o ficha' adds no new semantic value. With 100% schema coverage, the description does not need to elaborate 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 explicitly states the tool returns a ready-to-paste citation with a permanent link, verification date, and verifier, and clarifies the distinction between human-verified and imported data. It is specific about the output, though it does not explicitly contrast with sibling tools like 'dato_citable'.

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 when-to-use guidance: 'Usa esto cuando vayas a citar cualquier dato de esta fuente en una respuesta.' It does not mention alternatives or exclusions, which would further strengthen the guidance.

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?

With no annotations, the description bears the full transparency burden. It discloses that results include source and link to the full record, and implies a read-only search behavior. It doesn't over-promise, but omits details like pagination or auth; for a simple search tool this is adequate.

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?

Two sentences with the core action first, followed by data characteristics and practical use case. No filler or redundant restatement.

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?

The tool has no output schema, so it's important that the description communicates expected results—it does ('Cada dato viene con su fuente y el link al récord completo'). It covers topics and use case, though it could mention result format nuances or limitations. Still, for a 2-param search tool, this is sufficient.

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 description coverage is 100% for both parameters (pregunta and limite), so the baseline is 3. The description doesn't add parameter details, but it does enrich the understanding of what 'pregunta' should be via the example in the schema; no extra meaning needed.

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 uses a specific action ('Busca en el récord público verificado de Puerto Rico') and enumerates distinct content types (fuente primaria, contradicciones declarado-vs-entregado, citas ancladas al minuto), which clearly differentiates it from sibling tools like buscar_negocio or desiertos_salud.

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 states 'Ideal para verificar afirmaciones sobre agua, luz, FEMA, salud, presupuesto y promesas públicas de PR', giving clear use context. It doesn't formalize exclusions or alternative tool references, but the domain is well-scoped.

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?

With no annotations provided, the description carries the full burden and does well: it discloses that only counts are returned, never raw messages, and that data is aggregated by category and limited to a specific region and channel. It adds meaningful behavioral context beyond the schema.

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, front-loads the core purpose, and every clause earns its place: geographic scope, channel, aggregation, and intended use. No fluff or repetition.

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 low-complexity tool with one optional parameter and no output schema, the description is sufficiently complete. It explains the result shape (counts by category) and what is deliberately excluded (raw messages), which is enough for an agent 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?

Schema coverage is 100%, so the single parameter (dias) is fully documented in the schema. The description does not add extra parameter details, but none are needed; the baseline of 3 applies because the schema handles parameter meaning.

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 states what the tool does: it shows demand for services searched by real people in Cabo Rojo and western PR via *7711, aggregated by category. It uses a specific verb ('buscó'), names the resource, and the mention of aggregated counts distinguishes it from sibling tools like buscar_negocio.

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 it is useful for understanding what is missing in the local market, providing clear context for when to use it. It does not name alternatives or exclusions, but the unique purpose is apparent enough.

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?

With no annotations, the description carries the full burden. It discloses the data source (NPPES registry crossed with Census) and the ranking methodology (deficit vs metro area, poverty, geographic isolation, federal HPSA). However, it does not specify the response format, the definition of 'casi cero', or potential edge cases like empty results, leaving some behavioral ambiguity.

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, tightly packed with key information: what is returned, how it is ordered, and the data provenance. No filler or repetition; the unique-dataset claim is the only slightly promotional element but still informative.

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 two parameters, no output schema, and no annotations, the description explains the core function and sorting logic. However, it leaves out expected output fields (e.g., municipality name, risk score, provider counts) and does not clarify the threshold for 'casi cero', making it less complete for an agent trying to interpret results.

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 provides descriptions for both parameters (especialidad and limite), covering 100% of the schema. The description adds no additional parameter-level detail beyond the title's mention of 'por especialidad', so it does not exceed the baseline.

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 states the tool returns municipalities in Puerto Rico with zero or near-zero providers of a medical specialty, sorted by a specific risk score rather than alphabetically. This specific content and the 'único dataset de este tipo para PR' claim distinguish it clearly from sibling tools like buscar_negocio or eventos_proximos.

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 implies the tool is for finding medical specialty deserts in Puerto Rico but provides no explicit guidance on when to use it versus alternatives. Sibling tools cover different domains (business search, recalls, citable data, local demand, events), so the use case is contextually distinct, but no exclusions or alternatives are named.

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?

There are no annotations, so the description carries the full transparency burden. It usefully explains the source, edition dates, matching to NPPES, and comparison behavior between editions. It does not fully describe the output format or no-match behavior, but it exposes the important semantic behavior more than most descriptions do.

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?

Despite being long, the description is organized with clear labels like Cobertura and Útil para, and each sentence contributes useful information. Slight deduction for the hyperbolic 'ÚNICO EN EL MUNDO' opening and the regulatory citations, which are not genuinely necessary for invocation.

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 one parameter and no output schema, it is still impressively complete: it states scope, coverage, offline limitation, and what the tool can answer. It does not describe the exact return shape or no-match behavior, but an uneven gap for a lookup tool of this kind.

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 description coverage is 100% and the schema already documents that the parameter accepts a physician name or 10-digit NPI. The tool description adds no additional parameter-level guidance, 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 clearly states a specific action and resource: consulting published provider directories of Puerto Rico health plans across time, matched to the federal NPPES registry. It also enumerates distinctive capabilities—network retention, town listing, published phone, and edition-to-edition disappearance—which distinguishes it from the other tools in the sibling set.

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 provides explicit use cases ('verificar si un médico acepta tu plan, detectar teléfonos publicados que no corresponden, y medir exactitud de directorio') and coverage details. It does not name alternative tools or state when-not-to-use it, so it stops just short of full usage guidance.

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?

No annotations are provided, so the description carries the transparency burden. It discloses the nature of the data (published, family-friendly, upcoming) and the included fields (place, time, price), but does not explicitly state it is a read-only list or mention any limitations such as pagination or filtering. This is moderate behavioral insight.

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 a single, efficient sentence that packs essential details—geographic scope, family-friendly filter, time window, and included fields—without any redundant words. It is well-suited for quick agent understanding.

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 tool with one optional parameter and no output schema, the description adequately explains the content of the result (place, time, price). It could explicitly state that it returns a list, but the intent is clear and the parameter is fully described in the schema.

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 covers 100% of the parameter 'dias' with a clear description of its range and default. The tool description adds no extra semantic meaning beyond the schema, so the baseline of 3 applies.

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 the tool's subject—published and family-friendly upcoming events in Cabo Rojo and western Puerto Rico—and includes place, time, and price details. It clearly distinguishes from sibling tools like business search or recall checks, but lacks an explicit verb such as 'lists' or 'retrieves.'

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: to find upcoming family-friendly events in a specific geographic region. It does not explicitly mention alternatives or exclusions, but none of the sibling tools overlap with events, so the context 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?

There are no annotations, so the description carries the full behavioral burden. It fully explains that output is aggregated only, that personal raw text is never exposed, and that ordering is by distinct people rather than question count. This is exactly the kind of useful behavioral detail that prevents misuse and misunderstanding.

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 compact and front-loaded: the behavior is in the opening sentence, and each subsequent sentence adds a new, non-redundant detail about purpose, ordering, and privacy. Nothing is repeated or wasted.

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 no annotations and no output schema, the description covers the essential information: what is returned, how it is ordered, that it is aggregate-only, and the privacy constraint. Some ambiguity remains around what 'esta fuente' refers to outside the title, and the exact output shape is not described, but the tool is simple enough that this is a solid definition.

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 covers 100% of parameter descriptions, so the baseline is 3. The description adds meaningful context by clarifying how results are aggregated and ordered by distinct persons, which affects how 'limite' and 'municipio' should be interpreted. It does not write out parameter-level details, but it compensates 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 clearly states the specific behavior: it returns requests that real Puerto Rican people made to this source that the source could not answer, aggregated by category and municipality. It identifies a distinct resource ('esta fuente' and its gaps) and defines what makes it different from more general demand 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 says it serves two explicit purposes: knowing when NOT to trust this source, and detecting local demand nobody is serving. It gives clear context for when to call it, but it does not explicitly name or exclude alternatives like the sibling tool demanda_local.

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?

With no annotations, the description carries the full burden. It discloses that this is a read-style query ('Devuelve...'), that results are limited to manual verification rates, and importantly that imported federal-registry records are not counted as verified. This is meaningful behavioral detail, though output shape and any other constraints are left implicit.

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 a tight three-sentence structure: first the result, then the important usage caveat, then the technical qualification about federal imports. Every sentence earns its place and there is no redundant 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 tool with only two optional parameters and no output schema, this is complete: it explains what the tool computes, what the fields mean, the key interpretation caveat, and how the query is scoped. An agent has enough to call it correctly and interpret the result.

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%, so the parameters already have clear meanings. The description adds little beyond repeating 'municipio o categoría', which is acceptable but not additional semantic value.

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 percentage of manually verified records over 90 and 365 days. The title 'Qué tan fresco está este dato' and the error-margin framing make its purpose as a freshness/source-quality tool unambiguous.

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 a clear intended scenario: check this before citing data from an ámbito, and explicitly flag low pct_frescas_90d values. It does not name alternative sibling tools or explicit when-not-to-use conditions, but the usage context is concrete enough.

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