MCP x402 - Evidence-Backed Web Verification

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

With no annotations provided, the description carries the full behavioral disclosure burden. It goes beyond a bare 'analyzes a URL' by clarifying that the report is limited to the page content, that no external sources are consulted, and that an optional objective only guides focus and is not treated as proof. These are meaningful behavioral constraints. It could still mention error/redirect behavior, but the essentials are 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 two sentences, front-loaded with the main action and scope, and each sentence contributes: the first covers behavior and output, the second covers the emotional input and the alternative. There is no verbose filler or repetition of schema field details.

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 names the report sections: resumo, factos, riscos e ações recomendadas. It also explains the optional objective's role and the external-source limitation. The only minor gap is the lack of information about failure cases or the exact structure of the returned report, but this is a relatively simple tool and the description conveys enough for correct use.

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 explains both parameters well. The description adds the narrative that 'objetivo' is optional and guides focus but is not treated as proof, which slightly supplements rather than repeats the schema. This meets the baseline for high schema coverage.

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 ('extrair'), a precise resource ('página HTTP ou HTTPS pública'), and a concrete deliverable (relatório com resumo, factos, riscos e ações recomendadas). It also explicitly distinguishes itself from verificar_condicoes by saying it does not consult external sources, so an agent can tell the tools apart.

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 opens with 'Use para...' and explicitly directs users to verificar_condicoes for criteria-based decisions with evidence. It also notes that the tool does not consult external sources, giving a clear exclusion rule and an explicit alternative. This tells an agent when to use the tool and when to prefer a sibling.

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 so well: it discloses that the tool returns a concise answer based on model knowledge, does not open URLs, and does not confirm current facts. It does not mention limitations like potential inaccuracy or straightforward refusal for irrelevant prompts, but it covers the critical constraints.

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 compact, scannable sentences with no filler. The primary purpose is front-loaded, followed by clear behavioral notes and sibling references, all earning their 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?

For a simple single-parameter tool with no output schema and no annotations, the description fully covers what the agent needs: purpose, usage limits, behavioral expectations, and routing to alternatives. 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?

Input schema provides 100% coverage with a clear description of the 'prompt' parameter. The tool description adds only a slight contextual nuance by tying the prompt to general questions but mostly restates what the schema already documents. 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?

States a specific verb ('Use para perguntas gerais') and resource scope, immediately clarifying it is a general-knowledge IA tool. It distinguishes itself from siblings by explicitly naming those alternatives, so an agent can tell it apart without inspecting other schemas.

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 explicit usage context ('perguntas gerais que não exijam pesquisa web em tempo real') and clearly lists which alternative to use for page analysis or criteria verification. This leaves no ambiguity about when to select this tool over its siblings.

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 behavioral burden. It discloses the 'incerta' outcome for unproven conditions, lists the return fields (source, verifiedAt, verificationId, pageHash), and explicitly states the public-page requirement. This gives a reliable baseline of expected 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 description is two sentences: the first gives the purpose and scope, the second summarizes the return values and key ambiguity rule. Every sentence earns its place with no filler or unnecessary 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?

Since there is no output schema, the description properly explains what will be returned per condition and the overall result semantics. It leaves some minor aspects unstated (e.g., exact format of proof text, behavior on unreachable pages), but it is sufficiently complete for an agent to call the tool 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 description coverage is 100%, so each parameter (url, contexto, condicoes) already has a meaningful description. The tool description adds little beyond restating the 1–10 condition count and the public/HTTP restriction, which are also present in the schema. This sits at the baseline of 3.

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 verb (testar), a specific resource (página HTTP/HTTPS pública), and the expected output (confirmada, rejeitada, incerta). It distinguishes the tool from siblings like analisar_url and consultar_ia by focusing on explicit condition verification rather than general analysis or AI consultation.

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 opens with 'Use para', giving immediate situational context: test 1 to 10 explicit conditions on a public page. It does not explicitly name alternatives or exclusions, but the purpose is clear enough that an agent can infer when to prefer this over the sibling tools.

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