Desafío Comunicación

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

With no annotations provided, the description carries the full burden of behavioral disclosure. 'Devuelve' and 'públicas' suggest a read-only, public information lookup, which is useful, but the description does not mention authentication, error behavior, return format, or any limiting conditions.

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 sentence that front-loads the action and target resource, with no filler or redundant information. It is concise and effective.

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 optional, self-documenting parameter, the description provides the essential content scope: identity, positioning, and public capabilities. It lacks explicit output format and usage guidance, but the low complexity and clear scope make it 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?

Schema description coverage is 0%, so the description could have added parameter context, but it does not mention the language parameter. However, the single parameter is fully self-documenting in the schema with const 'es-ES' and a default, leaving no real ambiguity.

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 starts with 'Devuelve' (returns) and clearly specifies the resource: identity, positioning, and public capabilities of Desafío Comunicación. This makes the tool's purpose unambiguous and distinguishes it from siblings like get_contact and get_service.

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 should be used when company-level identity, positioning, or public capabilities are needed, but it never explicitly states when to use it over sibling tools. No alternatives or exclusions are mentioned, so the agent must infer the usage context.

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 transparency burden. It clearly discloses that the tool only returns contact channels/next steps and does not send messages or forms, implying a read-only, side-effect-free operation. However, it does not mention permissions, response behavior, or operational constraints beyond that.

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 short sentences with no filler. The main action is front-loaded in the first clause, and the behavioral reassurance is compactly stated in the second sentence.

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 is simple and parameter-light, but with no annotations and no output schema, the description should explain more about what the agent should send in 'intent' and what the 'siguiente paso' looks like in the response. These gaps are material to 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?

Schema description coverage is 0%, and the description does not explain either parameter ('intent' or 'language'). Although both parameters are optional and 'language' is fixed to es-ES, the description fails to compensate for the schema gap by saying what 'intent' means or how it affects the result.

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 opens with a specific verb ('Devuelve') and a clear resource: public contact channels and the next step to contact. The explicit negation 'No envía mensajes ni formularios' distinguishes it from sending actions, but it does not directly reference sibling tools such as get_service or search, so it falls just short of full differentiation.

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?

There is no explicit guidance for when to use this tool versus its siblings. 'Para contactar' implies the use case, and the negation explains what the tool will not do, but no conditions, prerequisites, or alternative tool recommendations are provided.

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 full burden of behavioral disclosure. It does communicate that the operation returns a verifiable record and depends on a stable identifier, which implies a read-style retrieval. However, it does not disclose possible errors, authentication requirements, or what 'verificable' means in practice, 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 a single, compact sentence with no filler. It front-loads the action and target object, and the stable-identifier qualifier adds relevant precision without 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?

For a simple two-parameter retrieval tool with no output schema, the description together with the input schema gives an agent enough to call it correctly: provide a stable service_id and optionally set language. The main gap is explicit guidance about which sibling to use when the identifier is unknown, but the low complexity makes the definition reasonably 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?

Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics for service_id by calling it the 'identificador estable', and the schema itself documents language with a const/default. Still, the description does not explicitly name parameters or explain constraints beyond what the schema already provides.

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 ('Devuelve'), a specific resource ('la ficha verificable de un servicio de Desafío'), and the lookup mechanism ('por su identificador estable'). This clearly distinguishes it from siblings like search, recommend_service, and list_case_studies: it is a direct retrieval by stable identifier.

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 'por su identificador estable' implies that this tool should be used when the caller already has a stable service ID and needs the verifiable record. However, it does not explicitly say when not to use it or contrast it with search or recommend_service, so the routing guidance is only implicit.

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 transparency burden. It usefully discloses that only published case studies are returned, which is a non-obvious behavioral constraint beyond the tool name, and it clarifies that filtering is optional. It does not discuss ordering, pagination, or errors, but for a read-only list operation the key behavior is disclosed.

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, front-loaded with the core purpose and followed by the filtering options. Every word contributes meaningful information with 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?

The description is adequate for a simple listing tool, but it lacks return-shape details and no output schema is provided to compensate. It also omits guidance on how this tool relates to search or when its optional filters should be supplied, leaving some context to inference.

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 0%, so the description must compensate. It clarifies that service_id and technology are optional filters, adding meaning beyond the raw schema. However, it does not explain limit or language semantics, though those are partially self-evident from their names and schema constraints.

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 action: list case studies, restricted to published ones only. It also states the optional filters (service or technology), which distinguishes it from broader sibling tools like search or single-entity getters such as get_company and get_service.

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?

No guidance is given about when to use this tool versus alternatives such as search or get_service. The optional filtering behavior is mentioned, but there is no when-to-use, when-not-to-use, or explicit contrast with sibling tools.

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 behavioral burden. It discloses that the output is indicative, explains fit, and does not promise results, which is valuable and non-obvious. It does not go into details about side effects or internal behavior, but for a recommendation tool these added caveats are meaningful.

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 dense sentence that communicates both the core action and the important caveat about output reliability. Every clause adds value, and there is no filler 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 tool with no annotations, no output schema, and no parameter descriptions, the description is too sparse. It lacks details about the output structure, optional parameters, or how the recommendation should be presented to users. The available information covers only the basic purpose and a caveat about 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?

Schema description coverage is 0%, so the description must compensate. It only maps to the 'need' parameter through the phrase 'relaciona una necesidad', but provides no semantics for 'language' or 'company_type'. The optional parameters remain unexplained in both the schema and description.

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 explains the tool's function: relating a need to Desafío's public services. It also differentiates itself from siblings like get_service and search by framing the output as an advisory match rather than a direct lookup or raw search. The 'need' concept is specific enough to guide an agent.

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 usage when an agent has a stated need and wants to identify matching services, but it does not explicitly state when to prefer this over search or get_service. No exclusions or alternative routing are provided, so the guidance is only 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 behavioral burden, but the verb 'Busca' and the phrase 'información pública aprobada' reasonably imply a read-only operation over public data. However, it does not disclose result behavior, pagination, or any special handling of the type parameter, leaving a moderate transparency gap.

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 concise sentences with no repetition or filler. The primary function is front-loaded, and the usage condition is stated immediately after, making it easy for an agent to parse quickly.

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 description provides a clear purpose and a key usage condition, but it lacks parameter-level guidance and return-value information, especially given the absence of an output schema and annotations. It is adequate for a simple search call but leaves the agent to infer important details such as how type narrowing works and what the response 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 description coverage is only 25%, so the description needed to compensate by explaining parameters like type and limit, but it does not. The description's list of resource types loosely maps to the type enum, but it offers no guidance on values, defaults, or how query, limit, and language interact.

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 names the resources searched: services, cases, FAQs, and approved public information. It further distinguishes the tool by stating it is for when the user does not yet have a known identifier, which separates it from the get_* 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 explicitly says to use this tool when the need does not map to a known identifier, which is clear usage guidance. It does not name sibling alternatives or specify when not to use it, but the identifier condition gives an agent enough context to choose between search and get_* tools.

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