DETRAN RJ: Nada Consta - Veículo Apreendido

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

Annotations provide idempotentHint=true, indicating calling with no args returns a link (non-mutating). The description adds context about permanent vs session-only tokens and the token payload, which is helpful. However, it doesn't disclose what happens after authentication (e.g., response format, whether it persists state), but the annotations already cover the key idempotency trait, so this is acceptable.

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 target audience and main action, then explains two usage paths. It's concise but slightly dense with parenthetical details; still, every part earns its place and it's easy to scan.

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 with one optional parameter, and the description covers the auth flow fully: how to get token, permanent config, session pass, and no-args behavior. It lacks a return-value description but there's no output schema, and the context is sufficient for an agent to invoke correctly.

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. The description explains the 'token' parameter as an optional JWT for session-only login, but it doesn't detail the format or behavior when omitted beyond 'get the link'. This is sufficient for a single optional parameter, but it could be more explicit about the token's structure.

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: to authenticate users for MCP.AI, providing login instructions and supporting both permanent (config header) and session-only (token) authentication. It distinguishes itself from siblings by focusing on the authentication flow for IDE agents, though it doesn't explicitly compare to 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?

The description provides explicit usage guidance: it tells the agent to log in via browser, copy the token, and either add it to the server config for permanent access or pass it as a parameter for session-only login. It also gives the no-args option to get the link, covering all call variants clearly.

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, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds meaningful behavioral context by specifying the two output states (all connected vs missing credentials), which goes 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?

Two sentences, front-loaded with the core purpose, and every clause adds useful conditional detail. No wasted words or redundant restatement of the tool name.

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 parameterless status tool with strong annotations, the description covers the key return variants and connection states. It does not explain partial connection scenarios, but the two stated conditions are likely sufficient for most agent decisions.

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 100% schema description coverage, so there is nothing for the description to add about parameters. Baseline 4 is appropriate for a parameterless 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 returns connection status and URLs, with specific conditional outcomes (authenticated:true and empty pending[] vs connect_url). This is a specific verb+resource that distinguishes it from the sibling authenticate tool, which likely performs the actual authentication 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?

The description implies when to use the tool by describing its conditional behavior, but it does not explicitly state when to use it versus alternatives like authenticate. It explains what happens when credentials are missing but does not direct the agent to a follow-up action.

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 provide readOnlyHint, idempotentHint, and destructiveHint, which cover the safety profile. Description adds that it's a paid per-query service with credit, hosted by the platform, and that the client controls data under LGPD. This adds useful behavioral context about payment and legal responsibility, though it doesn't describe the exact output format or error behaviors. No contradiction with 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 paragraph with no wasted words. It is concise but packs essential context: the official source, the payment model, the data nature, and LGPD responsibilities. It could be slightly improved by separating the core function from the commercial/legal caveats, but it remains efficient.

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 has no output schema and moderate complexity with 5 parameters (3 required). The description covers the administrative context but leaves the query behavior and parameter specifics unaddressed. Since there is no output schema, the description should at least hint at what the response contains (e.g., status). The description is incomplete for an agent to effectively invoke the tool with correct input formats.

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 covers 5 parameters, but description provides zero parameter details. The schema itself only lists names and types, with no descriptions. The description does not clarify the roles of each parameter (e.g., what format placa, chassi, renavam should take, or when to use cpf/cnpj vs placa). Given 0% schema description coverage, the description fails to compensate, making parameter usage ambiguous.

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: to query DETRAN RJ for vehicle seizure status ('Nada Consta' check). It mentions the official source and the data type (non-confidential citizen-available data). It distinguishes from siblings by being a specific DETRAN RJ vehicle query tool, though it doesn't explicitly contrast with other potential vehicle 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 implies usage context (prequalified credit, per-query payment, client responsible for LGPD compliance) but does not explicitly state when to use this tool vs alternatives. It mentions the payment model and data responsibility, which suggests the commercial use case, but lacks explicit alternatives or exclusions.

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 only indicate not read-only, open world, not idempotent, not destructive, but the description adds critical context: invoke runs one-off even if not installed, returns connect/checkout links on auth/payment barriers, writes require owner/admin, and prompt library behavior. This goes well beyond 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 dense single paragraph with many sentences, but each sentence adds unique value. It is front-loaded with the core purpose and flow, though it could be structured with line breaks for readability. Not wasteful, but slightly long.

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 highly complex tool with 23 params and no output schema, the description covers the main workflows, permissions, auth/payment edge cases, and the prompt library. It does not describe exact return schemas for each action, but it provides enough context for an agent to select and invoke actions correctly.

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% with 23 parameters, and the description provides a conceptual map of the action enum and key workflow parameters (mcp_id, tool_id, arguments) but does not explain most parameters like limit, query, prompt_vars, conversation, or the exact JSON format of arguments. It compensates partially through the flow but leaves many parameters underspecified.

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?

Description clearly identifies the tool as the mcp.ai marketplace and the way to run MCPs, with explicit verbs: search, describe, invoke, install. It distinguishes from siblings by covering the full lifecycle and prompt library.

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 guidance: 'prefer invoke for a single/occasional use', 'Use install only to make an MCP PERMANENT', and differentiates subscribe/cancel, report_bug, request_mcp, list_tools. This tells the agent when to choose this tool vs alternatives.

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 cover safety (readOnly=false, idempotent=true, destructive=false). The description adds the requirement to include conversation for reproduction but doesn't disclose additional behavioral aspects. No 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?

Two sentences: first states purpose, second gives usage instruction. No fluff, perfectly front-loaded.

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 simplicity (3 params, no output schema), the description covers the main purpose and hints at important usage (conversation), but lacks parameter definitions. Annotations help, but completeness is moderate.

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 only explains the conversation parameter ('recent messages for reproduction'), leaving message and context undefined. This is insufficient compensation.

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 it reports bugs, missing features, or feedback, using a specific verb and resource. This distinguishes it from unrelated siblings like authenticate or 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 context is clear for when to use the tool (reporting issues), but it lacks explicit exclusions or alternative tool references. The instruction to include the conversation array 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 indicate read-only, idempotent, and non-destructive behavior. The description adds no extra behavioral details beyond what annotations provide, so it remains at the baseline for tools with 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 concise sentence with no unnecessary words or redundancy, making it highly efficient 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?

As a simple information-retrieval tool with no output schema, the description fully conveys its purpose. No additional context about return values or side effects 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?

There are no parameters defined, and the description correctly omits any mention. With zero parameters, the baseline is 4, and the description does not need to elaborate further.

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 exact function: showing the current MCP platform and adapter versions. It is specific and unambiguous, distinguishing it from other tools like authenticate or connect.

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 for when to use this tool (when version information is needed) and implicitly differentiates it from other operations, though it lacks explicit alternatives or exclusions.

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 signal readOnly, idempotent, and non-destructive behavior, and the description adds useful return-value context—the nature of the reported state and the specific fields included. This matches the annotations and provides more detail than structured metadata alone.

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, front-loads the action and object, and uses a concise enumerative structure for the returned state. Every phrase contributes meaningful information without repetition or 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 parameterless, read-only status tool without an output schema, the description is complete by specifying what the edges will contain: installed MCPs, connection status, connected accounts, and catalog tool counts. It gives an agent sufficient information to decide whether and when to call 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?

The input schema has zero parameters and is empty, so the baseline is 4; schema coverage is effectively 100%. The description does not need to document parameters because there are none 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 uses a specific verb ('Returns') and names the precise resource ('current toolkit state'), then enumerates the contents: installed MCPs, connection status, accounts connected, and catalog tool counts. This clearly distinguishes it from sibling actions such as authenticate, connect, and report_bug, which perform different operations.

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 makes it obvious that this is the read-only status query for overviewing toolkit connectivity and accounts, especially in contrast to mutating siblings like connect and authenticate. It does not explicitly state alternative or exclusion conditions, but the context is clear enough for an agent to select it appropriately.

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