Lightning · AI Visibility Data

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 covered. The description contributes the cost signal ('gratis'), telling the agent no purchase is triggered, but says nothing about result volume, pagination, or empty-result behavior.

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?

Front-loaded with the purpose and facets, then examples; every element is functional. The Spanish/English duplication is intentional bilingual support rather than padding, though it does double the surface area.

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 read-only search with no output schema and annotations covering safety, the entry is sufficient: facets, examples, and cost are all present. The only missing piece is routing guidance relative to listar_estudios.

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?

With a single parameter at 100% schema coverage, the baseline is 3; the description exceeds it by supplying concrete example query strings ('seguros de auto', 'protector solar', 'hoteles Córdoba', 'dermatitis') that demonstrate the acceptable granularity and mix of category vs. proper-noun inputs, which the schema's 'Texto a buscar' does not.

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?

States a specific verb (buscar) and resource (estudios) plus the searchable facets (industria, tema, producto, marca, país), which clearly conveys what the tool does. However, it does not distinguish itself from the sibling listar_estudios, leaving an agent to guess whether keyword search or plain listing is intended.

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 parenthetical '(gratis)' implies this is the free browsing path, which hints at a when-to-use context, and the query examples imply keyword-based retrieval. But there is no explicit statement of when to prefer this over listar_estudios or when to move on to comprar_estudio/ver_ficha, so usage is only implied.

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 safety is covered. The description adds genuine behavioral context beyond that: it costs nothing ('Gratis') and it details that guidance covers pricing, payment networks, the collection wallet, and x402 flow over both HTTP and MCP.

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 compact sentences, front-loaded with what the tool explains. The trailing bilingual '/ How to buy.' is redundant with the title, but overall the text is tight and wastes little space.

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 read-only informational tool with one optional enum param and no output schema, the description does a good job of previewing the content returned (price, networks, wallet, x402 steps) and noting it is free. The only real gap is the unexplained slug parameter.

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 0% and the single slug parameter is never mentioned in the description. While the enum values are self-describing topic slugs, the description adds nothing about what slug selects or that it is optional, so it fails to compensate for the total lack of schema documentation.

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?

States a specific verb+resource (explains how to buy a estudio) and enumerates the content covered: price, networks, payment wallet, and x402 steps over HTTP or MCP. This clearly separates it from a pure purchase action, though it never names the sibling comprar_estudio to make the distinction explicit.

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 word 'Gratis' implies this is the free informational counterpart to a paid purchase, which hints at when to use it, but there is no explicit when-to-use, no prerequisites, and no reference to comprar_estudio as the alternative. Usage is only weakly implied.

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 only flag readOnlyHint=false, openWorldHint=true, idempotentHint=false; the description adds substantial context beyond that: per-call USDC charging, exact price, the x402 challenge-and-retry flow, and the 7-day expiry on download links. It also discloses the full payload (tables plus raw model responses), which is exactly the kind of detail annotations cannot carry.

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 core is front-loaded and useful, but enumerating nine slugs each at 'USD 0.99' is pure padding that could be collapsed into one sentence. The trailing bilingual gloss ('/ Paid tool (x402): buy a full study.') is largely redundant with the opening line.

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 carries the return-value burden and does so: it names the deliverables, the download-link artifacts and their 7-day lifetime, and what comes back when payment is absent. Nothing needed to invoke or interpret the call 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?

Schema coverage is 100% and the single enum parameter is fully documented, so the baseline is 3; the description goes further by mapping each slug to its price, which is meaning the schema does not contain. The gain is modest because all nine prices are identical.

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?

States a specific verb+resource (comprar un estudio) and immediately frames it as the paid x402 purchase, which cleanly separates it from free siblings like ver_ficha, listar_estudios and buscar_estudios. The deliverables are enumerated, so an agent knows exactly what it is buying.

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?

Gives clear operative context: without payment the call returns x402 requirements, and an MCP client with a wallet pays and retries automatically — this tells the agent the call is safe to attempt blind. It never explicitly names an alternative (e.g. browse with listar_estudios/ver_ficha first), so it stops short of full when-not 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered; the description confirms 'free and read-only' and adds genuinely useful boundary context by stating that analysis numbers and findings exist only in the paid study, plus that price/purchase info is included. This is real behavioral detail beyond the 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?

The main sentence is front-loaded with verb, resource, and return contents, and every clause about the free/paid split earns its place. The trailing Spanish sentence is a light duplication but reasonable for a bilingual toolset.

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 usefully enumerates the returned fields (methodology, sample, models, question-universe, index, price), substituting for a missing return spec. Combined with the read-only annotation, an agent has enough to call it correctly, though the paid/free boundary of the index of analyses could be spelled out more precisely.

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%, so the id parameter and its valid values ('metricas', 'como-comprar', study slug) are already fully documented in the schema. The description only restates that the id comes from search, adding marginal value, 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?

States a specific verb and resource ('Returns the free sheet of a study by id') and enumerates the exact sections returned, which is far more informative than a tautology. It is not fully distinguished from the near-synonymous sibling ver_ficha, so the agent may still hesitate between the two.

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 parenthetical 'id (from search)' implies the id originates from the search/buscar_estudios flow, giving implicit usage context. However, there is no explicit when-to-use guidance or differentiation from siblings like ver_ficha, ver_metricas, or como_comprar.

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 establish readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds that the studies are free ('gratis') and inventories the catalog's sectors, but says nothing about pagination, result limits, or whether an empty catalog is possible — modest added value on top of the 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?

The verb and the core payload are front-loaded in the first clause, which is good structure. The nine-sector enumeration is long but genuinely informative, while the English gloss ('/ Lists available AI-visibility studies (free)') is largely redundant duplication of the Spanish opening.

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?

There is no output schema, so the description carries the burden of describing return fields — and it does, listing industry, country, date, response count, findings and price. Combined with annotations covering the read-only profile, the agent has enough to call 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 tool takes zero parameters, so the schema imposes no semantic burden and the baseline of 4 applies. The description correctly implies a no-argument, catalog-wide listing rather than suggesting any filtering.

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?

States a specific verb and resource ('Lista los estudios disponibles') and enumerates what each entry contains (industria, país, fecha, respuestas, hallazgos, precio), so the agent knows exactly what comes back. It does not, however, explicitly distinguish itself from the sibling buscar_estudios, which an agent could easily confuse with a plain listing.

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?

'Usala para ver qué datos de visibilidad IA de Argentina y el Cono Sur hay' gives a clear use context and sector coverage, but there is no statement of when NOT to use it and no mention of the obvious alternative, buscar_estudios. Usage is implied rather than routed.

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 safety is covered; the description goes beyond by disclosing the return shape ('ids to use with fetch') and the absence of cost ('free'), which the annotations do not provide. It still says nothing about result limits, pagination, or ranking behavior.

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 English sentence is front-loaded with the core purpose and the fetch handoff before the parenthetical detail. The fully duplicated Spanish translation roughly doubles the length without adding new information, which is unnecessary token cost for an English-reading agent.

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?

There is no output schema, so the description's note that results are ids to be passed to fetch is load-bearing and correctly included. For a single-required-parameter search tool, the remaining omissions (result count, no-match behavior) are minor.

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?

Only one parameter and schema description coverage is 100%, so the schema already defines 'query' with a concrete example. The description adds the facet vocabulary (industry, topic, brand, country) that tells the agent what kinds of strings are valid, which is a modest addition over 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?

Specific verb+resource: searches Lightning's AI-visibility studies, and spells out the supported facets (industry, topic, brand, country) plus the extra content types ('metricas', 'como-comprar'). It clearly states the output handoff (ids for fetch). It does not distinguish itself from the near-identical Spanish sibling buscar_estudios, nor from listar_estudios, so sibling differentiation is incomplete.

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 a usage chain ('returns ids to use with fetch') and a cost/safety signal ('free and read-only'), which implies retrieval-first workflows. However it never states when to prefer this tool over buscar_estudios, listar_estudios, ver_metricas, or the dedicated como_comprar tool that its own description says it partially covers — leaving real ambiguity.

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 covered. The description adds a useful negative scope disclosure (no analysis numbers, no findings), but says nothing about auth, cost, rate limits, or the shape of the returned sheet.

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 compact sentences, front-loaded with the core purpose and content list, followed by the exclusion. The trailing bilingual '/ Free study sheet.' is redundant with the opening 'Ficha gratis' and adds little, a minor structural blemish.

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, read-only tool with no output schema, the description enumerates the return contents well, which compensates for the absent output schema. Missing only an explicit pointer to comprar_estudio for the withheld findings.

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 enum-constrained slug parameter is fully documented in the schema. The description adds no syntax or format detail beyond the schema, so this sits at the baseline; the enum of legal slugs makes the schema self-sufficient, nudging it slightly above minimum.

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?

States a specific verb and resource ('Ficha gratis de un estudio') and enumerates the exact content it returns (methodology, sample, models, dimensions, indexes, Q&A examples). It partially differentiates from siblings by declaring what is NOT included, though it never names comprar_estudio or listar_estudios explicitly.

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 word 'gratis' plus the exclusion 'No incluye los números de los análisis ni los hallazgos' implicitly signals this is a preview, not the paid content owned by comprar_estudio. However, no alternative is named and no explicit when-to-use/when-not condition is stated, leaving the routing 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered upstream. The description adds a useful behavioral fact ('Gratis'), implying no purchase/permission barrier, but says nothing about the form or volume of the returned definitions.

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 compact sentences, front-loaded with what is defined and immediately followed by the free-access note. The bilingual gloss is minor redundancy but does not bloat the entry.

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 reference tool, the description covers purpose, scope of metrics, and cost. No output schema exists, but the enumerated metric list gives the agent enough to know what it will get.

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?

Zero parameters with a fully closed schema, so the baseline is 4. The description correctly implies a parameterless lookup and adds no param detail, which is appropriate 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?

States a specific resource ('Definiciones de las métricas Lightning') and enumerates the metrics covered (Protagonismo IA, mención, orden, Top1, citación, HHI, etc.), so an agent knows this is a reference/lookup tool. It does not explicitly contrast with siblings like ver_ficha, but the naming is concrete enough to distinguish.

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?

'Gratis' hints that this reference is free and thus usable without purchase flow, which implicitly routes away from comprar_estudio, but there is no explicit when-to-use or when-not statement, and alternatives are not named.

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