Consulta Veicular Nacional

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

Annotations already declare idempotent, non-read-only, non-destructive. The description adds context about permanent vs session-only behavior and link retrieval, which goes beyond annotations. No contradictions.

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 approximately three sentences but contains a long, run-on middle sentence. Information is useful but could be more tightly structured; some redundancy exists (e.g., 'paste it here').

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 simple single-parameter tool, the description covers the main behavior, modes, and parameter usage. It lacks explicit return value details, but no output schema exists and the behavior is sufficiently implied.

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 has one optional token parameter with no description (0% coverage). The description explains the token parameter semantics: call with {token} for session login, or no args to get the link, effectively documenting both states. It doesn't detail token format but compensates adequately.

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 authenticates to MCP.AI for IDE agents, describing the login flow (browser, token copy) and two usage modes. However, it does not explicitly differentiate from the 'connect' sibling, so it stops short of full distinction.

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 gives explicit guidance for two scenarios: using a config header for permanent connection versus pasting a token for session-only login, and no-args to get a link. It lacks explicit when-not-to-use alternatives to siblings, but provides clear 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?

Annotations already declare read-only and idempotent hints. The description adds value by explaining response variations (authenticated:true vs connect_url) and the presence of pending[]. This goes beyond annotations and gives useful conditional 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 well-structured sentences, front-loaded with the primary purpose and then providing conditional details without unnecessary fluff. Every sentence earns its 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?

Given the simple zero-parameter tool and no output schema, the description covers the main scenarios (all connected vs credentials missing). However, it omits potential edge cases like partial connection states, leaving minor ambiguity, so it is not quite a 5.

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 description has no parameter details to add. The baseline for 0 params is 4, and the description does not need to compensate for missing schema coverage since there is nothing to document.

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 returns connection status and URLs, using a specific verb and resource. It distinguishes itself from siblings like authenticate by focusing on status checking rather than the authentication action itself.

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 clear context on when to use it: to check if all providers are connected and to get URLs when credentials are missing. It does not explicitly mention alternatives or when-not-to-use, but the conditional behavior implies its role in a connection workflow.

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 provides substantial behavioral detail beyond annotations: invoke works even when the MCP is not installed, returns a connect link if auth is needed, returns a checkout link if payment is required, and does not bloat the toolkit. It also discloses that writes require workspace owner/admin. These align with annotations (e.g., readOnlyHint=false) and add rich context without contradiction.

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 dense and front-loaded, with the core purpose first, then the flow, caveats, and prompt library. Every sentence adds value, but the single-paragraph wall of text could be better structured with sections or bullet-like separation to improve scannability for an AI 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?

Given the tool's complexity (14 actions, no output schema), the description covers the main flow, edge cases (auth, payment, not installed), permissions, and the prompt library. Minor gaps remain: the 'resume' action is not mentioned, and return value shapes are not described, but overall the description is sufficient for an agent to select and invoke the 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?

With 23 parameters and 0% schema description coverage, the description carries the full burden. It explains the meaning of action values, tool_id, arguments, prompt_slug, prompt_vars, and others through the described flows. However, several parameters like conversation, report_context, and cancel_reason are only inferable from context, not explicitly described, so it is not exhaustive.

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+resource: 'The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It clearly distinguishes from siblings by naming its core capability discovery and execution flow, and enumerates distinct actions like search, describe, invoke, and install.

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 states when to prefer invoke over install ('prefer invoke for a single/occasional use'), describes the core flow (search → describe → invoke), and identifies when each action is appropriate (e.g., 'report_bug sends feedback; request_mcp asks us to build a NEW MCP'). It also notes permission requirements for writes, giving clear when-to-use vs alternative 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?

The annotations already indicate readOnlyHint=false, destructiveHint=false, and idempotentHint=true. The description adds a useful behavioral requirement: include the conversation array for reproduction. However, it does not disclose what happens after reporting, whether external submission occurs, or any side effects beyond 'report.' The description does not contradict the annotations, so it earns a mid-range score.

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 long and immediately states the purpose. Every sentence earns its place: the first defines what the tool does, the second gives essential reproduction guidance. No redundancy or filler.

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 feedback/reporting tool, the description covers the purpose and one key input (conversation). However, it omits what the 'message' field should contain and gives no indication of output or post-submission behavior. Since there is no output schema, the description should do more to complete the picture, but the tool's complexity is low, so the gap is not severe.

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 partially explains the 'conversation' parameter by calling it an array with recent messages, but it does not explain the required 'message' parameter or the optional 'context' parameter. Additionally, the schema defines conversation as type string (default '[]'), so calling it an 'array' introduces ambiguity. This is insufficient for a three-parameter tool.

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: 'Report a bug, missing feature, or send feedback.' It uses a specific verb ('report') and resource (bug/missing feature/feedback), and this distinguishes it from sibling tools like authenticate, connect, and marketplace. No ambiguity about what this tool does.

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 implicitly tells when to use the tool (for bugs, missing features, or feedback) and gives a concrete instruction: 'Include the conversation array with recent messages for reproduction.' It does not explicitly name alternatives or exclusions, but the use cases are clear enough for an agent to select this tool over unrelated 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?

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds specificity by naming 'MCP platform and adapter versions,' which is helpful, but it does not elaborate on output format or other behavioral aspects. This is adequate for a trivial version-check tool.

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, front-loaded sentence with no filler. Every word earns its 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 parameterless, read-only version-checking tool with strong annotation coverage, the description is fully sufficient. It states exactly what information is shown (platform and adapter versions) and implies no side effects.

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 there is nothing to misinterpret. The baseline for zero parameters is 4, and the description correctly implies no inputs are required.

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 with a specific verb ('Show') and target ('current MCP platform and adapter versions'). It is distinct from sibling tools which handle authentication, connectivity, marketplace, bug reporting, and vehicle queries.

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 version information is needed, but does not explicitly state when to use it versus alternatives or provide exclusions. Given the simplicity of the tool, the implied usage is acceptable but not explicitly guided.

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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context by detailing exactly what the returned state includes, which is beyond the annotation's boolean hints. It also implies no side effects, consistent with 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 description is a single, well-structured sentence that immediately states the core function and lists specific return items. Every word contributes value, and there is no redundancy or filler.

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 no parameters and a clear output description, the description is complete. It fully explains what the agent will receive, and the sibling tools are so distinct that no further disambiguation is needed.

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 description needs to add no parameter-level meaning. The schema is empty with 100% coverage, and the baseline for zero parameter tools is 4, which 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 returns the current toolkit state, enumerating specific content (installed MCPs, connection status, connected accounts, catalog tool counts). This distinguishes it from sibling tools like authenticate, connect, and show_version, which handle different concerns.

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 defines a clear scope (current toolkit state), making it obvious when to invoke it. It does not explicitly mention alternatives or when-not-to-use cases, but the sibling tools are sufficiently different that no exclusions are necessary.

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, idempotent, and non-destructive behavior. The description adds meaningful beyond-annotation context: the cost model, lack of credentials, public/official data source, and LGPD obligations for the controller. This is exemplary transparency.

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 front-loaded with the core purpose and data fields, followed by essential pricing and legal context. Each sentence earns its place; the legal/LGPD sentence adds important compliance guidance without being 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?

For a relatively simple query tool with strong annotations, the description covers the purpose, returned data categories, access/cost model, data source status, and legal responsibility. Without an output schema, its enumeration of fields provides adequate completeness. A minor gap is the lack of any pagination or response format detail, but that is not material here.

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 clearly indicates that 'Placa' is the lookup key and describes the response fields, but it does not explain the 'completo' boolean parameter or expected input formats. Partial compensation for an otherwise obvious parameter.

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 ('consultar') and resource ('veículo a partir da placa'), lists concrete data fields (marca, modelo, ano, cor), and clearly distinguishes this from sibling tools like authenticate or report_bug, which serve entirely different 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?

It clearly states the access model (no credentials, pay-per-query with prepaid credit) and the data's public nature, giving the agent enough context to decide when to invoke it. It does not explicitly mention when not to use it or name alternatives, but sibling tools are unrelated, so the context is sufficient.

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