DETRAN PI: Multas

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

Annotations indicate idempotent and non-destructive behavior. The description adds transparency by explaining that passing a token authenticates, while omitting it returns a login link. It also distinguishes between permanent and session-only connections. However, it doesn't detail any potential side effects (e.g., whether the token is stored server-side), which would be a minor gap, but the annotations already provide safety 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 clear and logically structured, but it contains some redundancy (e.g., 'log in in the browser, copy the access token' and later 'paste it here'). It could be condensed slightly, but the information is well-organized and not overly verbose, earning a score above mid-range.

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 absence of an output schema, the description covers the main functional outcomes: authenticating with a token and obtaining a login link. It does not specify the exact return format, but that is not strictly required. The context is sufficient for most use cases, making it complete enough for a tool with this 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?

The schema only defines 'token' as an optional string. The description significantly enriches this by explaining that providing a token authenticates directly, while omitting it triggers the retrieval of a login link. This clarifies the parameter's purpose and the behavior in both cases, fully covering the semantic 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: authentication for MCP.AI IDE agents. It explicitly mentions logging in, obtaining an access token, and using it either as a permanent header or session-only login. This is specific and distinct from the sibling tools (connect, detran_pi_multas_consultar, etc.).

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 concrete usage instructions: how to obtain a token (via browser), and two ways to authenticate (permanent config header or session-only token parameter). It also explains the behavior when no arguments are provided (getting a login link). While it doesn't explicitly compare to alternatives, it gives clear 'when and how' guidance, justifying a score above average.

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 (read-only, idempotent, non-destructive), the description adds behavioral detail by explaining the response when all providers are connected versus when credentials are missing, making the tool's behavior more transparent.

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 directly states the functionality in two sentences without extraneous detail. It is well-structured and easy to parse.

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 provides insight into return values and conditional scenarios, but does not enumerate all possible response fields or error cases. However, given the simplicity of the tool (no parameters, read-only status), the information is sufficient for basic understanding.

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 fully covers them and the description does not need to elaborate. The description does not add parameter-specific information since none exist, but the baseline for zero parameters is sufficient.

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 specifies conditional outcomes (authenticated true vs. missing credentials). It is specific and distinguishes the tool from generic status checks.

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 lacks guidance on when to use this tool versus other tools like authenticate or show_version. It does not mention prerequisites, alternatives, or specific use cases, leaving the agent to infer applicability.

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 explicitly notes it is hosted by the platform, requires no platform credentials, and involves a pay-per-query model with prepaid credit, which goes beyond annotations. It also clarifies data is public (same as available to citizens), not confidential, and addresses LGPD controller responsibilities, adding valuable context beyond readOnlyHint and idempotentHint.

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 paragraph covering key operational and compliance points without fluff. It is front-loaded with the main purpose, but the compliance and payment details could be seen as slightly verbose for a tool description, though they earn their place for transparency.

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 simple schema (2 string parameters, no output schema), the description covers the essential aspects of purpose, data source, payment, and legal context. It lacks explicit mention of input format or expected output, but this is acceptable for a query tool with a simple schema and existing annotations.

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 has 0% description coverage, so the description must compensate, but it does not explain what 'placa' (license plate) or 'renavam' (vehicle registration number) specifically mean or their formats. The tool name implies 'multas' consult, but no parameter-level detail is given, leaving the agent to infer from common knowledge.

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 PI for vehicle fines (multas) from an official source, which is specific and distinguishes it from generic siblings. While it doesn't name alternative tools for fine queries, the resource (DETRAN PI multas) is clearly identified.

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 for consulting official Brazilian vehicle fine information and highlights payment via prepaid credit, which is useful context. However, it doesn't explicitly state when to use this tool versus alternatives, such as other DETRAN state tools or general search tools, and doesn't mention prerequisites like valid plate and RENAVAM.

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 goes far beyond the sparse annotations by explaining one-off invocation behavior, credential/connect links, checkout/top-up flows, and that writes require workspace owner/admin. It also notes installed_in_toolkit vs installed_in_workspace flags, adding context not available in annotations. No contradiction with the annotations 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?

The description is a dense block of text, but it is logically structured: core flow, invoke vs install, billing/auth, and prompt library. Every sentence adds substantive detail, though a few line breaks would improve scannability. It is not overly verbose for the tool's complexity.

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 23 parameters, no output schema, and a multi-action surface, the description is remarkably complete. It covers the full lifecycle (search, describe, invoke, install, subscribe, cancel), auth requirements, billing fallbacks, and prompt-library operations, leaving little ambiguity about how to use 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?

With 23 parameters and 0% schema description coverage, the description carries a heavy burden. It explains the meaning of key parameters like action, mcp_id, tool_id, messages, and prompt_vars through workflow examples. However, many parameters (limit, immediate, tier_slug, report_context, etc.) are not explicitly addressed, leaving some semantics implicit.

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 marketplace is 'the in-platform catalog of every MCP/tool, AND the way to run them,' with a concrete core flow (search → describe → invoke). It distinguishes itself from sibling tools by covering the full catalog, running tools, and the prompt library, which no sibling handles.

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?

Explicit guidance is provided throughout: 'prefer invoke for a single/occasional use,' 'Use install only to make an MCP PERMANENT,' and 'list_tools lists what is callable right now.' It also differentiates subscribe/cancel, report_bug, and request_mcp, so the agent knows exactly which action to pick.

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 indicate readOnlyHint=false, destructiveHint=false, and idempotentHint=true. The description adds that the conversation array should be included for reproduction, giving some behavioral context. It does not contradict annotations and provides minimal but adequate transparency for a submission 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 two concise sentences with no redundant information. It directly states the purpose and provides a specific instruction, making it well-structured and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple and the description captures the core action, but it lacks details on what message should contain, what context means, or what the return value is (no output schema). This leaves some gaps for a fully self-contained description.

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 provides no parameter descriptions (0% coverage). The description only hints at the 'conversation' parameter ('Include the conversation array with recent messages') but does not explain the 'message' or 'context' fields. This leaves most parameter semantics unclear.

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.' It uses specific verbs and a clear object, distinguishing it from sibling tools like authenticate or connect.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool (when reporting bugs, missing features, or providing feedback) and provides a usage hint ('Include the conversation array'). It does not explicitly mention alternatives, but the sibling context makes the use case obvious.

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 destructiveHint=false, so the safety profile is covered. The description itself adds no additional behavioral detail beyond what the annotations state (e.g., no mention of return format or whether network activity occurs). It doesn't contradict annotations, and given the trivial nature of the operation, a score of 3 is appropriate.

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 the purpose. It's perfectly concise 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?

For a simple tool with no parameters and no output schema, the description adequately covers the purpose. It could mention what format the version info is returned in, but given the simplicity and the existence of annotations, it's sufficiently complete. A slight gap is the lack of any note about when the tool might fail or what it actually returns, but that's minor.

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 the schema fully covers this with no required fields. The description adds no parameter details because none exist. Per the rubric, baseline for 0 params is 4, and there's no deficit here.

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 a specific action ('Show') and the exact resource ('current MCP platform and adapter versions'). It is concise and unambiguous, and while it doesn't explicitly differentiate from all siblings, the specific mention of 'versions' makes its purpose clear enough to stand on its own.

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 gives no guidance on when to use this tool versus alternatives such as 'toolkit_info' or 'connect'. There is no mention of prerequisites, common use cases, or exclusions. For a simple version check, this may be acceptable, but still minimal that the tool's purpose is obvious.

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 value by specifying the actual content of the returned state (installed MCPs, connection status, accounts, catalog tool counts), giving the agent a clear picture of what to expect without needing to guess. No side effects are mentioned, which aligns with the read-only hints.

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 core action and resource, then provides a compact enumeration of the returned fields. Every word earns its place; there is no redundancy, fluff, or irrelevant detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, read-only tool with no output schema, the description fully covers what the tool does and what information it returns. The annotations handle the safety/behavioral profile, and the sibling list shows no overlapping tools that would require additional disambiguation. Nothing critical 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?

The tool has zero parameters, so schema coverage is trivially 100%. The baseline for no-parameter tools is 4; the description adds no parameter-specific details, which is appropriate because there are none to describe.

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 ('Returns') and resource ('current toolkit state'), then enumerates exactly what information is provided: installed MCPs, connection status, connected accounts, and catalog tool counts. This level of detail distinguishes it from sibling tools like show_version or marketplace, which serve different informational purposes.

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?

Usage context is implied but never stated explicitly. Given the toolkit-info nature, an agent can reasonably infer to call this when it needs an overview of MCP status or connected accounts, but the description does not mention when not to use it or point to alternatives such as show_version for version-specific details.

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