DETRAN MS Guia Flex: Licenciamentos

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

The annotations indicate idempotentHint=truelerance, but no readOnly or destructive hints. The description does not mention important behavioral aspects such as whether the tool requires user interaction (browser login), whether it returns a link, or the difference between permanent and session-only tokens. It adds some context about the server's config but lacks a full disclosure of side effects or requirements, which is notable given the interactive nature.

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 block with a lot of information compressed into a few sentences Drop. It is not overly long, but it mixes instructions, recommendations, and call syntax in a stream-of-consciousness style. The key info is front-loaded with the mention of MCP.AI and login, but the clarity could be improved with structured steps or bullet points.

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 one optional parameter)Skip no output schema, and annotations providing idempotency, the description covers the essential scenarios: permanent vs session login, and how to invoke. It could mention potential errors (e.g., invalid token) but it is largely complete for an interactive login 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?

Schema description coverage is 0%, but there is only one parameter (token). The description does explain the 'token' parameter's meaning as a JWT that the user pastes, but it could provide more detail on token format, expiration, or validation. Since the schema doesn't document it, the description provides some essential meaning, but not comprehensive.

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 that the tool is for authentication, specifically for MCP.AI in IDE agents, and explains the two modes: obtaining a login link and submitting a token for session login. It distinguishes itself from siblings like 'connect' by focusing on token-based authentication, though it doesn't explicitly mention 'connect' as an alternative.

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 guidance on when to use the tool: to log in via browser and copy the access token, and it recommends adding the token to the server's config for a permanent connection versus pasting it for a session-only login. It also tells the agent exactly how to call the tool: with { token: "<jwt>" } after the user pastes, or with no args to get the link.

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 safety profile is covered. The description adds context about the response structure (authenticated:true, empty pending[], connect_url) under different conditions, which goes beyond annotations and helps the agent understand what to expect without making a request. It does not contradict 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 two sentences, front-loaded with the core purpose ('Returns connection status and URLs') and immediately follows with conditional specifics. Every sentence adds value 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 no parameters and no output schema, the description covers the main states (all connected vs missing credentials) and describes the response fields (authenticated, pending, connect_url). It could specify the full JSON shape or mention partially connected states, but for a simple status tool it is adequately complete.

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 the schema coverage is trivially 100% (empty schema). Per instructions, baseline for 0 params is 4. The description does not need to explain parameters since there are none, and it appropriately focuses on the response behavior.

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 verb 'Returns' and the resource (connection status and URLs). It distinguishes itself from siblings like 'authenticate' by focusing on status retrieval rather than performing authentication. The conditional outcomes further specify the tool's behavior, making its purpose unambiguous.

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 context on when to use it (e.g., when all providers connected vs credentials missing) and implies it is for checking status, not for performing authentication. However, it does not explicitly state alternatives or when not to use it, though the sibling list makes the distinction obvious. It gives clear usage scenarios without explicit 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 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, is paid per query with prepaid credits, and clarifies data is not confidential and the client is the data controller under LGPD. This goes beyond annotations by explaining the commercial and legal context, which is useful for an agent deciding whether to invoke the 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 paragraph of three sentences, which is concise. It front-loads the core purpose (DETRAN MS Licenciamentos, consulta em fonte oficial) and then adds context about hosting, payment, and legal aspects. No wasted words, but the legal disclaimer (LGPD) might be less critical for tool selection and could be trimmed, though it's relevant for compliance.

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 (2 required params, no output schema, no enums), the description covers the purpose, payment model, and data sensitivity. However, it lacks parameter semantics (as noted) and doesn't describe the return format or any error conditions. Since there's no output schema, the description should at least hint at what the response contains (e.g., licensing status, fees, dates). The legal context is useful but doesn't compensate for missing operational details.

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 for the two parameters (placa and renavam). The description does not explain what these parameters are, their format, or how they relate to the query. It only mentions 'consulta' generically. For a Brazilian vehicle licensing query, placa (license plate) and renavam (vehicle registration number) are domain-specific, and the description fails to clarify them, leaving the agent to guess.

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 DETRAN MS licensing information from an official source, using the verb 'consultar' (consult) and specifying the resource (licenciamentos). It distinguishes itself from siblings by focusing on a specific Brazilian state (MS) and a specific domain (licenciamentos), though it doesn't explicitly name alternative tools for similar 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 context: it's a paid query service with prepaid credits, and it's for official Brazilian data available to citizens. However, it doesn't explicitly state when to use this tool versus alternatives, nor does it mention any prerequisites like authentication (though sibling tools include 'authenticate' and 'connect', suggesting a workflow). The guidance is implicit rather than 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?

Beyond annotations (readOnlyHint false, openWorldHint true), the description discloses key behaviors: invoke runs one-off without installing, returns connect/checkout links on missing credentials or empty wallet, and writes require owner/admin permissions. It also explains installed_in_toolkit vs installed_in_workspace flags, adding significant context.

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 structured, starting with the overall purpose, then the core flow, key behaviors, usage guidance, and prompt library. Each sentence adds value, though the phrase 'pontualmente' is awkward and the text could be tightened without losing information.

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 23-parameter tool with no output schema, the description covers the main flows: discovery, execution, installation, billing, reporting, and prompts. It omits specifics on return formats and some parameter semantics, but overall it gives a robust operational picture.

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 carries the full burden for 23 parameters. It explains the core action values (search, describe, invoke, install), tool_id, mcp_id, arguments, and prompt-related parameters, providing meaning beyond the schema. It omits details for some params like limit, immediate, and cancel_reason, but it covers the most operationally important ones.

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 the official mcp.ai marketplace, both a catalog and an execution layer for MCPs/tools. It uses a concrete verb-resource structure and lays out the core flow (search → describe → invoke), which distinguishes it from sibling tools like authenticate or toolkit_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 provides explicit guidance on when to use invoke vs install ('prefer invoke for a single/occasional use'), when to use subscribe/cancel, report_bug, request_mcp, and the prompt library functions. It also states that writes require workspace owner/admin, giving clear operational constraints.

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=false, idempotentHint=true, and destructiveHint=false. The description adds the context that a conversation array should be included for reproduction, which is useful behavioral information. However, it does not elaborate on side effects beyond annotations, so it adds some but not extensive value.

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 short sentences with no wasted words. It front-loads the primary purpose and adds one key usage detail. It is concise, readable, and structured effectively.

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 3 parameters and no output schema, the description covers the main purpose and the role of the conversation parameter. The context parameter is not explained, and the schema type for conversation is a string while the description calls it an 'array', a minor gap. Overall, it is sufficiently complete for an AI 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?

Schema coverage is 0%, so the description must compensate. It explains the 'conversation' parameter's purpose ('recent messages for reproduction'), but does not mention the 'context' parameter. The 'message' parameter is required and self-evident. Partial compensation yields a mid-range score.

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 a specific verb and resources: 'Report a bug, missing feature, or send feedback.' This is unambiguous and distinguishes it from the sibling tools, none of which are related to bug reporting.

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 the tool: reporting bugs, missing features, or feedback. While it doesn't explicitly mention alternatives or exclusion criteria, the sibling set is unrelated, so the implied usage is sufficient. No additional guidance is needed.

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 establish readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the specific behavioral detail that it returns both MCP platform and adapter versions, which is context beyond the annotations. It does not contradict 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, front-loaded sentence that is appropriately sized for the tool's simplicity. Every word earns its place; there is zero waste.

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 version-inspection tool with 0 parameters, strong annotations, and no output schema, the description covers everything needed. It is complete and leaves no 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?

The tool takes 0 parameters, so the schema is empty. The description is not required to explain parameters. A baseline of 4 is applied per the rubric since no parameters 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 'Show the current MCP platform and adapter versions' uses a clear verb ('show') and specifies the exact resource (MCP platform and adapter versions). It is concise and unambiguous, and the sibling tools (e.g., authenticate, connect, toolkit_info) are sufficiently different in purpose that this description does not need to explicitly differentiate them.

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 a simple, read-only informational use case. There are no explicit when/when-not statements or alternatives, but the tool's purpose is so self-contained that explicit usage guidelines would add little value. The sibling tools are not close enough to require explicit 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description's 'Returns' language strongly corroborates a safe, side-effect-free operation with no contradiction. The description adds valuable behavioral context by specifying exactly which state dimensions are exposed (installed MCPs, connection status, accounts, catalog tool counts), going beyond what the annotations state. A full mark would have been possible if it had also mentioned error behavior when MCPs are unreachable, but for a read-only introspection tool this is strong.

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 sentence that front-loads the core idea ('Returns the current toolkit state'), immediately follows with a colon, and delivers three crisp, parallel details in a list format ('installed MCPs', 'connection status', 'accounts connected', 'catalog tool counts'). There is not a single wasted word, and the structure mirrors the expected return object's shape, making it trivially parseable.

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 tool with zero parameters, a small return surface, and thorough annotations covering the safety profile, the description carries exactly the right weight. The only gap is the lack of mention of failure modes (e.g., what happens when an MCP is unreachable mid-scan, or whether this triggers any network calls that could hang). However, given the 0-param surface and the strong annotation coverage, the description is nearly complete for practical purposes.

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 parameters and 100% schema description coverage, the rubric's guidance is to baseline at 4 since there's semantic burden carried by the description (there is no parameter schema to annotate). The description correctly focuses its energy on cataloging what the return value contains rather than trying to explain inputs that don't exist. This is exactly the right allocation of effort.

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?

Uses a specific verb ('Returns') plus a concrete resource ('current toolkit state') followed by three precisely enumerated facets: installed MCPs, connection status, associated accounts, and exposed catalog tool counts. This level of specificity clearly distinguishes it from siblings like show_version (which returns version info) and connect/authenticate (which are state-changing operations). The description makes it unmistakable what this tool does and doesn't do relative to its siblings.

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 (whenever an agent needs a snapshot of the toolkit state) but provides no explicit when-not-to-use guidance or named alternatives. Given the existence of similarly-introspective siblings like show_version, explicit exclusionary language (e.g., 'for version-only info, use show_version instead') would have elevated this. The usage context is clear but entirely implicit.

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