Diogo AI URL Analysis

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

Annotations are absent, so the description must carry the full burden. The description discloses that it 'extrai' a page and produces a report, which implies a read-only, non-destructive behavior. However, it does not mention potential limitations like JavaScript rendering, authentication requirements, or rate limits, which would be valuable for an agent. It provides basic transparency but lacks depth.

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 lists the output components (resumo, factos, riscos, ações) and mentions the target resource. It is front-loaded and wastes no words, making it highly concise and well-structured.

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 that the tool is relatively simple (two parameters, one required) and there is no output schema, the description is mostly adequate. However, it does not specify what constitutes a public page, whether the tool handles non-HTML content, or how the report is structured beyond the listed components. With no annotations or output schema, the description is the only source of context, and it leaves some gaps.

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%, meaning both parameters (url and objetivo) are already described in the schema. The description adds little beyond the schema: it doesn't explain how the 'objetivo' parameter influences the analysis or provide examples. Since the schema covers the parameters, the description only provides marginal value, so baseline 3 is correct.

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 specific: it states the verb 'extrai' (extracts), the resource 'página web pública' (public web page), and clearly differentiates from siblings by focusing on producing a report (resumo, factos, riscos, ações). This is a clear purpose that distinguishes from consulting an AI or verifying conditions.

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 (analyzing a public web page and generating a report), but it does not explicitly contrast with sibling tools or state conditions when it should not be used. Guidance is implied rather than explicit, so a score of 3 is appropriate.

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 of behavioral disclosure. It states the tool sends a question and returns a response, but doesn't disclose any potential side effects, rate limits, authentication requirements, or error behavior. For a tool that makes an external API call, this is a notable 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 a single, efficient sentence that conveys the core purpose without waste. It's appropriately sized for a simple tool with one parameter.

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), the description is somewhat adequate, but it lacks context about the nature of the AI response, potential delays, or how it relates to the sibling tools. For a tool that calls an external service, more context about expected behavior would be valuable.

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 schema already documents the 'prompt' parameter. The description adds minimal value by implying the prompt is a question, but doesn't provide additional context like expected format, language, or constraints beyond what the schema states.

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 clear verb ('Envia') and resource ('uma pergunta para a OpenAI'), and indicates the result ('devolve a resposta'). It distinguishes the tool as a general AI query tool, though it doesn't explicitly contrast with siblings like analisar_url or verificar_condicoes, which likely have more specific purposes.

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 no guidance on when to use this tool versus the sibling tools. It doesn't mention any context, prerequisites, or exclusions. An agent would have to infer from the sibling names that this is for general queries while others are for specific tasks, but the description itself offers no such direction.

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 discloses the output behavior (decision of confirmed, rejected, or uncertain with textual evidence). It does not mention side effects, but as a read-only check on a public page, this is largely implied. No annotations are present, so the description carries the burden adequately.

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, concise sentence that effectively communicates the purpose and output. It is well-structured and free of 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 sufficiently explains the tool's functionality and output format. It does not detail edge cases or error handling, but for a simple verification tool, this is adequate. The lack of an output schema is compensated by the description of the decision types.

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 parameter descriptions in the schema already cover URL, optional context, and conditions. The tool description adds no extra meaning beyond that, so it relies on the schema. With 100% schema coverage, 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: verifying whether a public web page meets concrete conditions and returning a decision with evidence. It distinguishes itself from sibling tools by focusing on condition checking and decision 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 does not explicitly indicate when to use this tool over the sibling tools 'analisar_url' or 'consultar_ia'. It implies usage for condition verification, but lacks explicit differentiation or scenarios.

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