DETRAN MG: Registrar Intenção de Venda de Veículo

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

Annotations already indicate idempotent, non-read-only, non-destructive behavior. The description adds valuable context beyond annotations: calling with no arguments returns a login link, while supplying a token performs a session-only login. It also clarifies the permanent config-header option, which is useful behavioral information.

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 but information-dense, front-loading the purpose and then presenting the two usage options in a logical order. Every sentence contributes meaningful guidance 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?

For a simple one-optional-parameter auth tool with no output schema, the description covers the full workflow: browser login, obtaining the token, permanent config approach, session-only approach, and how to invoke the tool in each case. 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?

Schema description coverage is 0%, so the description must carry the burden for the token parameter. It does this excellently by explaining the token is a JWT pasted after browser login and that omitting arguments returns the link. This fully compensates for the schema's lack of property descriptions.

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 identifies the tool as an authentication flow for MCP.AI on IDE agents, with a specific verb ('log in') and resource (access token for this server). It does not explicitly contrast itself with the sibling tool 'connect,' so it misses the top score for sibling differentiation.

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 gives clear context about when this tool is relevant (MCP.AI for IDE agents) and describes two distinct usage modes: permanent via config header and session-only via pasted token. However, it does not explicitly state when to avoid this tool or use an alternative sibling, so exclusions are missing.

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 behavioral details beyond the readOnly/idempotent annotations, such as the conditional return values (authenticated, pending, connect_url) and the distinction between all-connected and missing-credentials scenarios.

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 concise and well-structured: two sentences, the first states the primary function, and the second details conditional behavior. Every sentence contributes to understanding.

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 lack of an output schema, the description fully explains the return values and their meaning (authenticated, pending, connect_url) and covers the two main scenarios, making it complete for the tool's simplicity.

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, so the schema is fully covered (100%). The description adds no parameter-specific meaning because none exist; this 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's purpose: 'Returns connection status and URLs', which immediately conveys the main function.

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 (e.g., for checking connection status) but does not explicitly mention when to use this tool versus alternatives, nor does it provide any exclusions or prerequisites.

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?

Even with annotations (readOnlyHint=true, idempotentHint=true), the description adds meaningful behavioral context: the service requires prepaid credit ('pague por consulta com crédito pré-pago'), does not use platform credentials, returns only non-confidential public data available to citizens, and imposes LGPD compliance responsibilities on the client. The 'Registrar' wording sits slightly awkwardly with the readOnly annotation, but the overall query framing ('consulta em fonte oficial') is consistent, so no hard contradiction is flagged.

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 reasonably compact, covering purpose, cost, source, and legal considerations in about four sentences. However, the phrasing is choppy with comma splices and run-on clauses ('Hospedado pela plataforma, sem credenciais da plataforma, pague por consulta com crédito pré-pago'), and it mixes policy and compliance information into a single paragraph. It is not overly long, but the structure could be better organized for parsing.

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 complex tool—22 parameters, 14 required, no output schema, no enums—the description covers the legal and billing dimensions well but leaves operational gaps. It tells the agent about cost, data-privacy obligations, and official sourcing, but never explains what the query returns, what constitutes a successful request, or what the 14 required parameters represent contextually. It is minimally viable for invoking the tool but far from complete for an agent needing to fill out 22 fields accurately, especially without any output-format documentation to compensate.

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 schema description coverage at 0% across 22 parameters and no compensation in the description, this is a significant gap. The description does not explain any parameter's semantics, formatting, or valid values. While parameter names like 'placa' and 'chassi' are self-evident to Portuguese speakers, critical fields like 'crv', 'renavam', 'hodometro', and date-time parameters ('datahora_hodometro') lack any format or constraint guidance that the description could have provided.

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 and resource: 'Registrar Intenção de Venda de Veículo, consulta em fonte oficial' (Register Vehicle Sale Intent, query on official source). This clearly identifies the domain (vehicle sale intent registration in DETRAN MG) and the source type. However, the confusion between the imperative 'Registrar' (register, implying write) and the tool name's 'consultar' (query) muddies the exact action, preventing a perfect score.

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—when you need to query official Brazilian sources for a vehicle sale intent, subject to payment ('pague por consulta com crédito pré-pago')—but provides no explicit when-to-use vs. when-not-to-use guidance and names no alternatives. The usage context is implied rather than directly stated, so an agent must infer from the billing and official-source hints.

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 runtime behaviors: invoke works even when the MCP is not installed, runs the tool one-off without bloating the tool list, returns a connect link when credentials are needed, and returns a checkout/top-up link when payment is required. It also notes that writes require workspace owner/admin and that search/describe flag installation status. 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 long but information-dense and front-loaded with the tool's dual purpose. It earns its length given the multi-action scope and edge cases covered. A bulleted or more segmented structure would improve scannability, but every major sentence contributes meaningful guidance.

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 23-parameter tool with no output schema, the description covers core flows, permissions, auth/payment edge cases, and the prompt library. It omits return-shape details for search/list_tools and does not mention the 'resume' action or several optional parameters, but it is largely complete for typical usage and gives enough context to operate 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?

With 0% schema description coverage and 23 parameters, the description must compensate. It does explain the main action values (search, describe, invoke, install, subscribe, cancel, request_mcp, list_tools, prompt actions) and ties tool_id/mcp_id/arguments to the core flow. However, many parameters (limit, immediate, tier_slug, cancel_reason, report_context, prompt_body, etc.) are left unexplained, so parameter semantics are only partially supplied.

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 explicitly identifies the tool as 'the official mcp.ai marketplace' that both catalogs and runs MCPs, and lists concrete capability requests it covers ('find an MCP that does X', 'consulta um CPF'). It outlines a clear core flow (search → describe → invoke) and distinguishes this broad orchestrator from sibling tools by describing its scope.

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 when-to-use guidance: prefer invoke for one-off/single use, use install for permanent toolkit additions, use list_tools to see what is immediately callable, and use request_mcp when nothing fits. It also explains the auth/payment retry flow and mentions permission requirements for writes, making alternatives and prerequisites clear.

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=false (mutation), idempotentHint=true (repeatable), and destructiveHint=false (no destruction). The description adds the context that the conversation array is needed for reproduction, which is useful. However, it does not disclose what happens after submission (e.g., external API call, stored feedback) or any rate limits. The description adds some value beyond the annotations but does not fully characterize the 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, front-loaded with the purpose, then a concise usage hint. Every word earns its place; there is no superfluous content. It is appropriately sized for the tool's simplicity.

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 3 parameters, no output schema, and is a mutation (readOnlyHint=false). The description provides minimal context: it does not clarify what each parameter is for, what the tool returns, or any side effects. Given the complexity, this is insufficient. The mention of 'conversation array' is helpful but does not cover other parameters or expected outcomes.

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% – the description does not name or explain any of the actual parameters (context, message, conversation) except implicitly mentioning 'conversation array'. It does not clarify the meaning of 'message' (presumably the bug description) or 'context'. With three parameters and zero coverage, the description fails to compensate, leaving the agent to guess parameter semantics from names alone.

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.' This uses a specific verb ('report') with a resource ('bug/missing feature/feedback'), and the intent is unambiguous. No sibling tools overlap with this functionality, so it naturally distinguishes 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 offers practical guidance: 'Include the conversation array with recent messages for reproduction.' This tells the agent what content to provide. While it does not explicitly mention when not to use this tool or alternatives, the sibling tools are unrelated (e.g., authenticate, marketplace), so no competitive alternatives exist. The context is clear enough for correct 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?

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. However, the description adds no additional behavioral context beyond that, such as whether it makes network calls or the format of the version info.

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 is front-loaded and every word contributes to the meaning. There is 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?

For a simple version-display tool with no parameters and no output schema, the description combined with the informative annotations is fully complete. It clearly states what the tool does, and no further context 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 0 parameters, so the schema provides no parameter details. The description does not need to explain parameters, and the baseline for 0-parameter tools is 4. It adds minimal semantic value but is sufficient 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 uses the specific verb 'Show' and identifies the resource as 'the current MCP platform and adapter versions.' This clearly distinguishes it from sibling tools like authenticate, connect, and marketplace, none of which seem related to version info.

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 it does not explicitly state when to use this tool versus alternatives. No exclusions or alternative tools are mentioned, so the guidance is only implied by the tool's purpose.

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 (readOnlyHint=true, idempotentHint=true, destructiveHint=false) already establish this as a safe read operation. The description meaningfully adds what data is retrievable — installed MCPs, connection status, account mappings, catalog tool counts — giving agents a sense of return-value richness beyond the bare annotations, though it doesn't enumerate possible status value vocabularies.

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?

A single 24-word sentence with a front-loaded verb and a colon-initialized list of the four components of the return value. Every word earns its place; the structure perfectly mirrors the conceptual breakdown of the toolkit state.

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 status tool with no output schema, the description covers the essential expectations: what information is retrievable and at what granularity. Minor gaps remain — possible connection status values are unspecified, and the term 'catalog tools' lacks definition — but these don't significantly hamper an agent's understanding of this simple 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 zero parameters and vacuous 100% schema coverage (empty properties object), there is nothing to document and the 0-param baseline of 4 applies. The description rightly focuses on the return value rather than parameters since none 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 uses a specific verb + resource ('Returns the current toolkit state') followed by a concrete enumeration of exactly what is included: installed MCPs, their connection status, connected accounts, and catalog tool counts per MCP. This specificity clearly differentiates it from siblings like 'authenticate' or 'report_bug'.

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?

When to use the tool is implied by its clearly stated purpose — an agent would reasonably call this to inspect toolkit state, especially before other operations. However, there is no explicit when/when-not guidance and no mention of alternatives among the six sibling tools.

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