Infrações da PRF

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

Adds context beyond annotations by explaining browser login requirement and persistence differences (permanent vs session-only). However, it does not describe the return value or error behavior, which is a gap given no output 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?

Three sentences deliver purpose, configuration, and usage in a compact form. Slightly dense but each sentence adds value; could be better structured with bullet points, yet it's not overly verbose.

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 output schema and a simple tool, the description covers the main scenarios: link generation, session login, and permanent configuration. It lacks explicit mention of return values or edge cases, but is reasonably complete for a straightforward auth tool.

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 has an optional token string with no description; the description explains that token is a JWT for session auth and that omitting it returns a login link. This compensates well for the 0% schema description 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 clearly states the tool is for authentication to MCP.AI, with instructions for browser login and token handling. It distinguishes itself from siblings by focusing specifically on login/token configuration, though it doesn't explicitly contrast with the 'connect' sibling.

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?

Provides explicit usage paths: permanent configuration via header, session-only via token parameter, and link generation via no args. It also recommends the best option (permanent config) and explains the alternative, giving clear when-to-use 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 indicate read-only and idempotent behavior, so the bar is lower. The description adds meaningful behavioral details by describing conditional outcomes: returns authenticated:true with empty pending[] when all connected, and returns connect_url and per-install URLs when credentials are missing. This goes beyond the annotations and provides useful state-dependent behavior, though it does not cover all possible intermediate states.

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 exactly two sentences, front-loaded with the primary purpose and then expanding into key conditional behaviors. Every sentence contributes value, no irrelevant details or repetitions.

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 (no parameters, no output schema, annotations cover safety), the description does a solid job explaining the two main return scenarios. It lacks a comprehensive schema of return fields, but for a status-checking tool, the provided details about authenticated, pending, and URLs are adequate. It could be more complete by describing partial connectivity, but the essentials are covered.

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 has zero parameters, so the baseline is 4 per the rubric. The description does not need to explain parameters; it instead focuses on the return values, which is appropriate. No parameter-related gaps exist.

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 as 'Returns connection status and URLs', which is a specific verb+resource and distinguishes it from sibling tools like 'authenticate' that perform actions. It also elaborates on two distinct return states, making the purpose unmistakable.

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 this tool is used to check connection status, but it does not explicitly state when to use it versus alternatives like 'authenticate' or other siblings. The conditions (all providers connected vs. credentials missing) give some usage context, but no direct guidance on when to choose this tool over others.

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?

Beyond the annotations, the description discloses key behavioral traits: invoke runs one-off without installing, writes require workspace owner/admin, credential/payment obstacles return actionable links, and search/describe flag installation status. This is rich transparency for a tool with minimal 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 description is long, but the tool is complex and every sentence carries meaningful guidance. It is front-loaded with the core purpose and flow, though a structured format (bullets or sections) would improve scannability.

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 hub tool with no output schema, the description covers the core flows, permission requirements, and failure paths comprehensively. It falls slightly short on explaining all action variants (e.g., resume, immediate) and return behavior, but is robust for selection and 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?

With 0% schema coverage, the description compensates by explaining the main action parameter and the roles of mcp_id, tool_id, arguments, and prompt_vars. However, many of the 23 parameters (limit, immediate, tier_slug, cancel_reason, prompt_slug, request_details, etc.) are not explicitly described, leaving gaps.

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 purpose: the official mcp.ai marketplace that catalogs and runs MCPs/tools. It distinguishes itself from sibling tools by detailing its unique capabilities (search, describe, invoke, install, prompt library) and core flow.

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 provides explicit guidance on when to use each action: 'prefer invoke for a single/occasional use' vs 'install only to make an MCP PERMANENT', plus instructions for billing, bug reports, and requests. It even handles edge cases like credentials and wallet balance with a retry flow.

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, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds valuable context: it's hosted by the platform, requires no platform credentials, charges per query with prepaid credits, and clarifies data is not confidential. This goes beyond annotations by explaining the commercial and legal aspects.

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 paragraph with several sentences, each adding distinct information: purpose, hosting, payment, data source, and legal responsibility. It's front-loaded with the core purpose. Slightly verbose due to legal disclaimers, but each sentence earns its place for a tool with compliance implications.

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 has only 2 required parameters, no output schema, and clear annotations, the description covers the essential context: what it does, how it's accessed, cost model, and legal obligations. It doesn't explain return values, but without an output schema, that's less critical. The description is complete enough for an agent to decide when and how to use it.

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 mentions 'placa' and 'renavam' implicitly by stating the tool queries infractions, but it doesn't explain the format or meaning of these parameters. The description adds minimal value beyond the schema, which only lists types. A 3 is appropriate because the parameters are self-explanatory (license plate and RENAVAM number) but the description doesn't explicitly clarify them.

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 queries Brazilian Federal Highway Police traffic infractions from an official source, with a specific verb ('consultar') and resource ('infrações'). It distinguishes itself from siblings by specifying the data source and official nature, though it doesn't explicitly contrast with other 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 explains the payment model (prepaid credits) and legal context (LGPD), which implies when to use it (for official Brazilian traffic infraction data). However, it doesn't explicitly state when not to use it or mention alternatives, and the sibling tools are mostly unrelated (auth, marketplace, etc.), so differentiation is less critical.

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 idempotentHint=true and destructiveHint=false, so the safety profile is known. The description adds one behavioral instruction about including the conversation for reproduction, but does not disclose side effects, response behavior, or other execution details beyond what annotations provide.

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 front-loads the action and includes a practical usage note. No wasted words.

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 reporting tool, the description covers the essentials but leaves gaps: the context parameter is undefined, and the conversation parameter's expected format (string containing JSON) is not clarified. No output schema exists, so return behavior is unknown, but annotations mitigate some safety concerns.

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%, so the description must compensate. It explains the conversation parameter as an 'array with recent messages,' but the schema defines it as a string (likely serialized JSON), which could confuse. The context parameter is not explained at all. The message parameter is implicitly understood from the purpose.

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 purpose with specific verbs and resources: 'Report a bug, missing feature, or send feedback.' It distinguishes itself from sibling tools by being the only reporting/feedback mechanism.

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?

Provides clear context on how to use: 'Include the conversation array with recent messages for reproduction.' It implies this is for bug reports/feedback and does not mention conflicting alternatives given the distinctly different 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the detail that it returns both platform and adapter versions, which is useful context, but it does not disclose any additional behavioral aspects like return format or potential dynamic values.

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, grammatically complete sentence of nine words. It is front-loaded and contains no wasted content, effectively conveying the action and target.

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 zero-parameter, simple read-only version tool with no output schema, the description fully covers the purpose. It explains what the tool shows (current platform and adapter versions) and is complete in the context of its low complexity.

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 has zero parameters and schema coverage is 100% (empty properties). The description correctly implies no parameters are needed. With no params to explain, the baseline is 4, and the description adds no extra meaning but doesn't need to.

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 the specific verb 'show' and clearly identifies the resource: 'the current MCP platform and adapter versions.' This precisely distinguishes it from sibling tools like authenticate or marketplace, which serve entirely different functions.

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 for checking version information, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. Given the simplicity and unique purpose among siblings, the implied context is clear but not 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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description's 'Returns' aligns safely. The description adds concrete details about the data returned (MCPs, status, accounts, tool counts), which is useful context beyond the generic annotations. No hidden behaviors or side effects are mentioned, but none are expected for a read-only state query.

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, well-structured sentence that front-loads the key action ('Returns the current toolkit state') followed by a concise list of content items. Every word contributes meaning with no fluff or 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?

Given that this is a simple no-parameter query with no output schema, the description fully captures what the tool returns. It covers all relevant aspects of the response, making it complete for an AI agent to understand the tool's purpose and expected 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?

The input schema has zero parameters, so the baseline is 4. The description doesn't attempt to explain parameters, which is correct since there are none to clarify. No further semantic enrichment is 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 the specific verb 'Returns' and specifies the resource as 'current toolkit state', enumerating exactly what is included: installed MCPs, connection status, connected accounts, and catalog tool counts. This clearly distinguishes it from sibling tools like authenticate, connect, and marketplace.

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 — one would call this to inspect toolkit state — but it does not explicitly state when to use it versus alternatives, nor does it mention any exclusions or prerequisites. The context of sibling tools suggests distinct roles, but the description itself lacks direct guidance.

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