DETRAN AM: Licenciamento Anual

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

Annotations indicate an idempotent, non-destructive operation. The description adds behavioral context beyond annotations: explains that calling with no args returns a login link, and calling with a token establishes a session. It also notes the permanent vs session-only distinction, which is useful and consistent 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 somewhat verbose but each sentence contributes value: context, best practice, invocation modes. It is front-loaded with the core action ('log in in the browser') and structured logically. Minor redundancy ('copy the access token' and 'after the user pastes') but overall 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?

Given the tool's complexity (two authentication modes, config vs session), the description adequately covers the essentials: how to initiate login, how to provide the token, and the permanent vs session-only distinction. It does not mention return values or error handling, but no output schema exists, and the focus is on usage.

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 compensates by explaining the 'token' parameter: it's a JWT obtained from the browser and used for session-only login. It also clarifies the no-arg invocation to get a link, giving the parameter semantic meaning beyond the bare 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's purpose: authenticating the MCP server for IDE agents. It specifies the exact actions (log in, copy token, call with token or no args) and distinguishes from siblings like 'connect' by focusing on authentication flow rather than general connection.

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 clear when-to-use context: for IDE agents needing authentication, with best-practice guidance (config header for permanent connection vs session-only token). It does not explicitly compare with sibling tools like 'connect' but gives sufficient direction on invocation modes.

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 behavioral detail beyond these annotations by explaining the conditional return states (authenticated:true with empty pending[] when all providers are connected, and connect_url with per-install URLs when credentials are missing). This provides useful context without contradicting 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 only two sentences, front-loaded with the main purpose ('Returns connection status and URLs'), and each clause adds essential detail about the two states. There is no waste 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?

With no output schema, the description properly explains the return values for both scenarios, covering the main behavior. The annotations handle safety properties, and the simple tool with no parameters requires no further explanation. It is complete and self-contained.

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 is trivial. With no parameters, the baseline is 4, and the description correctly avoids adding parameter semantics that don't 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 clearly states the tool 'Returns connection status and URLs' and elaborates on two specific states, giving a specific verb and resource. However, it does not explicitly distinguish itself from sibling tools like 'authenticate' or 'marketplace', so it lacks 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?

The description implies usage (when you need connection status) and provides clear context about the two return states, but it does not explicitly say when to use this tool versus alternatives. No exclusions or alternative tool references are mentioned, so guidance is implied 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?

Annotations already declare it read-only, idempotent, and non-destructive. The description adds useful non-annotation details: payment requirement, data from official public sources (not confidential), and LGPD responsibility. 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 moderately wordy, covering multiple operational and legal aspects in three sentences. It front-loads the purpose but includes extra details (LGPD, payment) that, while useful, could be more succinct for an 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?

The tool has only two parameters and lacks an output schema, so the description should explain what the response contains (e.g., vehicle licensing status). It fails to do so, and also omits parameter hints, leaving the agent under-informed for a successful invocation.

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%, and the description gives no explanation of 'placa' or 'renavam'. It doesn't compensate for the lack of parameter documentation, leaving the agent to infer meaning from cryptic names.

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 'Licenciamento Anual' (annual licensing) from DETRAN AM, using the verb 'consulta' and specifying the resource. It distinguishes itself from unrelated sibling tools by focusing on a specific Brazilian state agency's data.

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 provides operational context: hosted on the platform, no platform credentials needed, pay-per-query with prepaid credit. It doesn't explicitly discuss alternatives, but no similar siblings exist, so the guidance is adequate for an isolated tool.

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?

Although annotations lack read-only, the description explicitly discloses write behaviors, auth requirements ('Writes (install/uninstall/subscribe/cancel and the one-off install behind invoke) require workspace owner/admin'), and side effects such as invoke returning a connect link for credentials or a checkout link for payment failures. It also explains how invoke runs one-off without bloating the toolkit, adding significant behavioral context beyond 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 long but front-loaded with the core purpose and flow, and every section adds useful context for a multi-action hub. It is organized around the core flow, then KEY behavior, then use cases, then prompt library. While somewhat dense, the structure helps navigate the complexity; a bulleted list for actions would improve scannability, but each sentence contributes to the tool's 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?

For a tool with 23 parameters, an outputless schema, and a wide action enum, the description covers many main flows but leaves notable gaps: the `resume` action is entirely absent, most parameters are unexplained, and return shapes for search/list_tools are not described. It does describe several important outcomes (connect link, checkout link, shareable link, permanent install), making it moderately complete but not sufficient for such a compound 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% across 23 parameters, so the description must compensate by explaining parameter usage. It only narratively covers the `action` values and vaguely references `tool_id` ('pick the right tool_id') and `mcp_id` (implied by describe), but leaves most params undefined: `limit`, `query`, `arguments`, `immediate`, `tier_slug`, `cancel_reason`, `prompt_vars`, `conversation`, etc. The prose fails to map parameter names to their purposes, 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 identifies the tool as 'the official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It distinguishes this from siblings by presenting it as the central discovery, description, and invocation hub, and explains the capability requests it handles. The core flow (search → describe → invoke) provides a concrete sense of what the 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 provides explicit when-to-use guidance: 'Use install only to make an MCP PERMANENT... prefer invoke for a single/occasional use.' It lays out the core flow search→describe→invoke, explains when invoke vs install is appropriate, and notes list_tools for currently callable tools. It also covers subscribe/cancel, report_bug, request_mcp, and the prompt library flows, giving clear context on which action to choose.

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, idempotentHint=true, and destructiveHint=false. The description adds the behavioral context that the tool expects a conversation array for reproduction, which is useful beyond the schema. No contradictions 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 two sentences long, front-loaded with the primary purpose, and contains no redundant information. 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 simple tool with no output schema, the description covers the essential purpose and key parameter (conversation). It lacks details on the 'context' parameter and expected response, but the tool is straightforward enough that the description is mostly 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?

With 0% schema description coverage, the description compensates partially by explaining that 'conversation' should contain recent messages. 'message' is implied as the bug report text, but 'context' remains unexplained. The description adds meaning to two of three parameters but leaves a gap.

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 ('Report') and resource ('a bug, missing feature, or send feedback'). It distinguishes itself from sibling tools (authenticate, connect, marketplace, etc.) by focusing on issue 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 implies when to use the tool ('Report a bug, missing feature, or send feedback') and provides guidance to include the conversation array for reproduction. However, it does not explicitly mention alternative tools or when not to use it, though no sibling serves the same 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 already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds that it shows both platform and adapter versions, which is slightly more specific than the title, but it does not disclose any additional behavioral aspects like output format or potential latency.

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 is perfectly concise and front-loaded with the key verb and object. There is no wasted wording.

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, zero parameters, and rich annotations (readOnlyHint, idempotentHint, destructiveHint all provided), the description fully covers what an agent needs to know. No output schema exists, but the description even specifies the content (platform and adapter versions) which is sufficient.

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 does not need to explain any parameter semantics. The schema is empty and coverage is 100%, so the description adds no parameter details but none are needed. A score of 4 is appropriate as there is no burden on the description.

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: 'Show the current MCP platform and adapter versions.' It specifies the resource (versions) and the verb (show), and it is distinct from siblings like 'authenticate' and 'connect' which have different roles.

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 this tool: to check version information for troubleshooting or verification. It does not explicitly state when not to use it, but given its simple purpose, this is sufficient. No alternatives are needed because no sibling offers version info.

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 description's job to disclose side effects is light. However, it adds value by detailing what state is returned (MCPs, statuses, accounts, catalog counts), which is not in the annotations. It does not explicitly state 'no changes are made,' but the combination of annotations and 'Returns the current state' covers it. The description gives expected details without needing a warning.

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 lists all key elements without redundancy. It is front-loaded with the verb 'Returns' and immediately specifies the domain ('toolkit state'), then enumerates the specific components. Every word adds value, and the colon-separated list is readable. No fluff 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?

Given the tool has no parameters and no output schema, the description is complete in covering the tool's purpose and the content of what it returns. It addresses all aspects a user would need to know to decide to call it: what is returned, and implicitly, when to use it (e.g., to check connection status). The description is fully sufficient for a stateless read-only info 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?

There are zero parameters and schema coverage is 100% (empty schema), so the description needs no parameter clarification. The description remains useful by explaining what the returned information covers, adding context beyond the empty input schema. This is a case where the schema is trivial, so the description adds clarity about the tool's behavior. A score of 4 is warranted because it goes beyond the schema's silence by setting expectations about output content.

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 identifies the resource ('current toolkit state') with concrete details: 'installed MCPs, their connection status, the accounts connected to each one, and how many catalog tools each exposes.' It clearly distinguishes itself from siblings like `authenticate` and `marketplace` by focusing on state inspection. The purpose is immediate and 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?

Though there is no explicit 'when to use' or 'when not to use' statement, the description clearly implies a read-only diagnostic use case, contrasting with sibling tools like `connect` or `authenticate` that likely perform actions. The lack of parameters and readOnlyHint annotation further signal it should be used first to understand the environment. It lacks explicit exclusions or alternatives, but the context is strong.

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