DETRAN MG: Publicação de Dano (média e grande monta)

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

Annotations already declare the tool idempotent and non-destructive. The description adds meaningful context about the browser login flow, permanent vs. session-only token behavior, and how the token is supplied. It stops short of detailing token expiration or error behavior, but this is sufficient for the operation.

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 and generally well-structured, with the core action front-loaded. It includes a bit of contextual marketing ('MCP.AI for IDE agents...') but the rest is direct and informative, earning 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 simple tool with one optional parameter and no output schema, the description covers the key behaviors: how to authenticate, the two login modes, and the exact invocation patterns. It does not mention token expiration or error responses, but the provided information is sufficient for an agent to use the tool 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?

The schema only defines a generic 'token' property with no description (0% coverage). The description compensates by explaining that the token is a JWT pasted by the user, showing the exact invocation format { token: '<jwt>' } and clarifying that omitting it triggers the login-link flow. This adds essential meaning beyond the schema.

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, using specific verbs like 'log in' and 'authenticate.' It distinguishes itself from sibling tools (connect, marketplace, etc.) by focusing on token-based browser login and session/permanent configuration.

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?

Explicitly explains when to use a permanent header-based configuration versus a session-only token paste, and describes exactly how to call the tool with or without arguments to obtain the login link. This provides direct, actionable usage guidance and distinguishes the two modes 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=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds valuable behavioral detail: it explains the two distinct outcomes (authenticated with empty pending[] vs. returning connect_url) which is not specified in the annotations or schema. This goes beyond the structured metadata.

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 compact—only two sentences—and front-loaded with the core function. It avoids redundant phrasing and each sentence adds meaningful detail about the tool's behavior. 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?

Given the tool's simplicity (no parameters, no output schema), the description fully covers what the agent needs: it states the return type (status and URLs) and explains the conditional behavior. Combined with the annotations, the agent has a complete picture of the tool's purpose and output.

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 schema already provides full coverage (100%). Per the rubric, a baseline of 4 is appropriate when parameters are absent. No additional parameter description is needed, and the description does not attempt to explain nonexistent parameters.

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: 'Returns connection status and URLs.' It distinguishes from siblings like authenticate by specifying that it reports on connection state, and describes conditional behavior (connected vs. missing credentials). This is a specific verb+resource with clear scoping.

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?

While it does not explicitly state when to use this tool versus alternatives, the description implies it is for checking connection status, which is a clear use case. It would benefit from explicitly noting that it should be used to verify connectivity before attempting actions like authenticate, but the context is sufficient for an agent to infer appropriate usage.

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 no safety contradiction exists ('annotation_contradiction': false). The description adds genuinely useful context beyond this: the per-query prepaid credit model both warns agents about costs and implies the tool invokes a third-party paid API, alerts that no platform credentials are needed, and clarifies data origin/legality (official source, non-confidential, citizen-available). A slight deduction for not mentioning rate limits, which would be especially relevant given the per-query cost.

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 first sentence effectively front-loads the core purpose, which is ideal for agent parsing, and each piece of content (cost, data source, LGPD) adds non-duplicative info. However, the long sentences—especially the LGPD clause—create a dense paragraph that could confuse extraction. Overall, every sentence earns its place, but clarity slightly suffers from being an unbroken block of text.

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 read tool with strong annotations (read-only, idempotent), the description covers query scope, official source, cost, and legal usage, which is reasonably complete. However, it omits basic integration expectations like the response/return format (no output schema provided), potential error cases, and rate limits. The coating is adequate but leaves the agent to guess at the query result structure.

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 description coverage, the description carries the full burden of explaining the single 'placa' parameter, but it never mentions the expected Brazilian license plate format (e.g., Mercosur 'ABC1D23'). The parameter name is self-explanatory in Portuguese, but there is no guidance on case, special characters, or vehicle type, which this specific DETRAN query might be sensitive 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 a specific verb+resource structure ('consulta em fonte oficial' for 'Publicação de Dano' from DETRAN MG) and adds crucial scope differentiation by specifying 'média e grande monta' (medium and large vehicles). It clearly distinguishes itself from sibling tools (authenticate, connect, marketplace, etc.) which are platform-level. Minor deduction because it slightly restates the name rather than adding a behavioral example, and the scope distinction could be more explicit.

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?

There is no explicit when-to-use or when-not-to-use guidance, nor any mention of alternative tools for related queries (e.g., other DETRAN query types or vehicle categories). The description only implies the use case via the purpose. The cost warning ('pague por consulta') hints at being judicious with calls but never states when to prefer this tool or an alternative.

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 key behavioral traits beyond annotations: invoke runs one-off even if not installed, returns connect/checkout links for auth/payment, and install/uninstall/subscribe/cancel require owner/admin. This goes well beyond the generic readOnlyHint=false and openWorldHint=true, with 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?

The description is a single dense paragraph, but it uses arrows and action lists to guide reading. It is long because it must cover 14 actions, and every sentence adds a meaningful constraint or behavior. Minor over-verbosity around the prompt library section could be trimmed, but overall it is structured and 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?

The description covers the main workflows, permission model, auth/payment flows, and even the prompt library subsystem. It omits details on the 'resume' action and doesn't describe return value structures, but with no output schema and 23 optional parameters, the description provides enough high-level context for an agent to start using the tool safely.

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 description coverage, the description must compensate. It explains the action enum conceptually (search, describe, invoke, install, etc.) and references some params like tool_id and prompt_body indirectly. However, many parameters remain unexplained (e.g., immediate, tier_slug, conversation, prompt_targets), so agents may struggle to construct valid calls confidently.

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 crisp definition: 'The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It immediately states the tool's role and distinguishes it from sibling tools like 'authenticate' or 'report_bug' by positioning it as the central discovery and execution hub.

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 lays out the core flow: search → describe → invoke, and gives clear when-to-use guidance such as 'Use install only to make an MCP PERMANENT' and 'prefer invoke for a single/occasional use.' It also notes when to use request_mcp ('when nothing fits') and warns that writes require workspace owner/admin.

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=false, destructiveHint=false, openWorldHint=false, and idempotentHint=true. The description adds the conversation-array requirement for reproduction but does not explain submission effects, return behavior, or any rate limits. No annotation contradiction is present.

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 short sentences front-load the action and include actionable guidance without filler. Every word contributes to purpose or reproduction requirements.

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 3-parameter tool with no output schema, the description covers purpose and one parameter's semantics, but it omits what the caller should expect after reporting (e.g., confirmation, ticket ID, or error). This is adequate but leaves 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 0%, so the description must compensate. It explains the 'conversation' parameter's role ('recent messages for reproduction'), and 'message' is inferable from the purpose, but the 'context' parameter remains unexplained.

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 uses a specific verb ('Report') and identifies distinct resources: bug, missing feature, and feedback. This clearly differentiates 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 explicitly frames when to use the tool ('Report a bug, missing feature, or send feedback') and adds a concrete instruction to include the conversation array for reproduction. It does not list exclusions, but context is clear enough for an agent to select it.

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. The description adds the exact scope of what is shown (MCP platform and adapter versions) and is fully consistent with the annotations, with no hidden side effects mentioned.

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 contributes to clarifying the tool's purpose and return scope.

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, read-only version check with no output schema, the description is sufficient: it identifies the platform and adapter versions. One could ask for output structure, but it is not necessary for such a simple informational 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 tool has zero parameters, so the baseline of 4 applies. The description accurately conveys that the operation is a simple 'show current versions' call, and there are no parameter semantics left unexplained.

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 ('Show') and a precise resource ('current MCP platform and adapter versions'), making the tool's purpose unmistakable. It is also clearly distinct from sibling tools like authenticate, connect, 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 description clearly implies the tool should be used when version information is needed. It does not explicitly name alternatives or exclusions, but for a simple standalone version-info tool this is not a material gap.

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=false, so the safe, non-mutating nature is fully covered. The description adds value by disclosing what the returned state contains (MCPs, connection status, accounts, catalog tool counts), which goes beyond the raw annotation flags.

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 that front-loads the primary purpose ('Returns the current toolkit state') and then adds precise detail about each included element. Every word earns its place with no filler 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 its low complexity, zero parameters, and no output schema, the description sufficiently communicates what the tool returns and the scope of the information. It covers all meaningful aspects needed for an agent to decide to invoke it and understand 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?

The tool has zero parameters and no required input, so parameter semantics are not applicable. The description fully compensates by focusing on the output/state rather than inputs, matching the 0-param baseline of 4.

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 a distinct resource ('current toolkit state'), then enumerates exactly what is included: installed MCPs, connection status, connected accounts, and catalog tool counts. This clearly differentiates it from siblings like authenticate, connect, and show_version.

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 clear this is an inspection/reporting tool for toolkit state, implying use before or alongside connection operations. It does not explicitly state when not to use it or name alternatives, but the context is clear enough given the sibling set.

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