DETRAN RS: Veículo

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

Beyond the annotations (readOnlyHint: false, idempotentHint: true), the description reveals important behavioral traits: the distinction between permanent non-expiring connections via config and session-only login via token, and that calling with no args returns a link. It also mentions a two-step flow (user pastes token) and provides a security-relevant detail about the token being a JWT. This adds context beyond what annotations offer, though it could be more explicit about side effects or state changes.

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 informative paragraph that front-loads the purpose and provides step-by-step context without excessive fluff. While slightly run-on, it remains concise and every sentence adds value. The structure could be improved with bullets or shorter sentences, but it is far from verbose.

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 an authentication tool, the description covers the essential flow: browser login, token retrieval, and both permanent and session-based setups. It addresses the target audience (IDE agents like Cursor) and mentions specific commands. It does not cover error handling or expiration details, but given the simplicity of the tool and lack of output schema, 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 schema only defines 'token' as a string with no description, and the schema coverage is 0%. The description fully compensates by explaining that the token is a JWT, when to pass it (after user pastes), and what happens if omitted (get link). It clarifies the parameter's purpose and format, turning an opaque schema into a usable interface.

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 with MCP.AI for IDE agents by logging in via browser and obtaining an access token. It distinguishes itself from siblings like 'connect' by focusing specifically on authentication/login flow, and the verb 'authenticate' plus resource (MCP.AI for IDE agents) is specific and actionable.

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 guidance on two usage modes: permanent config-based authentication (adding to server config) and session-only login (passing token). It explains when to call with no args (get link) and with token (paste after receiving). However, it does not explicitly contrast with sibling tools or state when NOT to use it, missing a small opportunity for comparative guidance.

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 readOnly, idempotent, and non-destructive behavior. The description adds valuable detail on return values under different conditions (authenticated vs missing credentials), going beyond the structured 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?

Two concise sentences clearly state the purpose and conditional behaviors. Information is front-loaded and there is no redundant text.

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

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

For a simple status-check tool with no parameters and no output schema, the description covers the two main states and return contents. It does not mention error cases, but given the read-only and idempotent nature, this 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?

No parameters exist, so the baseline is 4. The description correctly omits any parameter details and focuses on the tool's behavior, which 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?

States the tool returns connection status and URLs, and differentiates outcomes for connected vs missing credentials. This is specific and distinct from sibling tools like authenticate or marketplace, which serve different 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?

Implies use for checking connection status but does not explicitly state when to call it over siblings. There is no guidance on when not to use it or when to use alternatives, though the context suggests it is the status check before authentication.

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?

With annotations already declaring readOnlyHint=true and destructiveHint=false, the description adds genuine context: prepaid per-query pricing model, no platform credentials required, data originates from official Brazilian government sources, and the client acts as data controller under LGPD. This is valuable because it flags cost (not exhaustive) and legal responsibility, which are meaningful behavioral constraints not captured by the structured 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?

It's one run-on passage cramming multiple concerns—function, hosting, payment, data sourcing, and legal compliance—into a single dense block. All content is relevant, but the structure forces the reader to parse through legal disclaimers to find the actual purpose. A short lead sentence stating the tool's function followed by a separate note on LGPD/compliance would be more effective.

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 and 6 parameters, the description could add value by hinting at the response shape (e.g., 'returns vehicle owner, make, model, year'). The legal and payment aspects are thoroughly covered, but the functional output is completely silent. For a single-purpose query tool, the missing functional detail is partially excusable, but the auth parameter flow ambiguity and lack of response description leave real gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate by explaining parameters, but it doesn't mention a single one. While 'placa' (license plate) and 'renavam' are self-explanatory, the optional auth parameters (login_cpf, login_senha, pkcs12_cert, pkcs12_pass) create ambiguity: are they alternatives or required together? When should an agent use CPF/password versus PKCS#12 certificate? The description entirely fails to clarify these semantics.

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 this as a vehicle query tool for DETRAN RS (Brazilian state traffic department) using an official source. The combinations of tool name ('detran_rs_veiculo_consultar') and opening phrase 'DETRAN RS: Veículo, consulta em fonte oficial' make the scope explicit. It loses a point because the description spends more prose on compliance than on stating exactly what the query returns or its full capability.

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

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

While it doesn't explicitly say 'use this when X, not Y', it conveys important usage constraints: it's a paid per-query service (prepaid credit), the caller must be LGPD-compliant, and it accesses non-confidential data from official Brazilian sources. The sibling tools (authenticate, marketplace, etc.) are sufficiently different that no exclusions are strictly necessary. However, the guidance is implicit rather than explicit about scenarios where this tool should be used or avoided.

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?

Reveals key behaviors beyond annotations: invoke runs unsinstalled MCPs one-off without bloating the tool list, returns connect/checkout links when credentials/balance are missing, and writes require owner/admin. The prompt library behavior is also disclosed, all consistent with 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 information-dense, front-loading the core flow and key invoke behavior before covering other actions and permissions. It earns every sentence, but the single-paragraph structure could be improved with breaks for readability.

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?

Covers the main actions, edge cases (connect/checkout links), permissions, and the prompt library comprehensively. However, it does not specify successful invoke return values or the exact output of search/describe, and not all parameters are explained, so it's not fully 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?

Despite 0% schema coverage, the description semantically explains the core parameters through the flow: action, query, mcp_id, tool_id, arguments, and prompt_* fields. However, some parameters like limit, immediate, and tier_slug are omitted, leaving ambiguity for those.

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?

Clearly states it's the official mcp.ai marketplace catalog and runtime, covering both discovery and execution of MCPs. It distinguishes the core flow (search→describe→invoke) and the prompt library, making it distinct from sibling tools like toolkit_info 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?

Explicitly instructs when to use invoke vs install, noting to prefer invoke for one-off uses and install for permanent additions. It also assigns each action a purpose (list_tools, subscribe/cancel, report_bug, request_mcp) and explains the connect/checkout link retry behavior.

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 declare readOnlyHint=false, destructiveHint=false, idempotentHint=true, openWorldHint=false. The description adds the behavioral context that the `conversation` parameter contains recent messages needed for reproduction, which is useful for the agent. It doesn't contradict annotations. It could mention side effects (e.g., whether a support ticket is created) but provides meaningful context beyond structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the purpose and a key usage instruction. Every word earns its place, no fluff. Excellent conciseness.

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 bug-reporting tool with simple string parameters and no nested objects, the description is mostly sufficient. It explains why the `conversation` parameter exists and what to include. However, it doesn't specify the format of `conversation` (JSON string) or what happens after reporting (e.g., response format), but since no output schema exists, some credit is given. It's adequate but not rich.

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, but it only mentions the `conversation` parameter, not `message` or `context`. The message parameter is required and obvious, but the description doesn't explain formatting for `conversation` (e.g., JSON array of strings) or what `context` is for. It partially compensates but leaves gaps. Baseline 3 seems fair.

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 ('Report') and identifies the resource ('a bug, missing feature, or send feedback'), clearly distinguishing it from siblings. It doesn't explicitly differentiate from sibling tools, but the purpose is unambiguous. A small deduction because it doesn't explicitly mention alternatives, but the action is clear.

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 ('Report a bug, missing feature, or send feedback') and gives one usage instruction ('Include the conversation array...'). However, it doesn't explain when not to use it or point to alternatives among siblings. It's adequate but lacks explicit exclusions or alternatives, earning a 3.

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 covered. The description adds the version-scope detail but does not disclose output format, potential failure modes, or any other behavioral nuance. No contradiction exists.

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, repetition, or irrelevant detail. Every word contributes to understanding the tool's function.

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, parameterless, read-only version tool, the description covers the essential purpose, and annotations handle safety semantics. The lack of an output schema is not a serious gap, though an explicit note about the exact return format could make it fully 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 takes zero parameters, so the input schema is complete and there is nothing for the description to clarify. Per the baseline for zero-parameter tools, this 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 uses a clear, specific verb ('show') and identifies the exact resource: 'current MCP platform and adapter versions.' This makes the tool's purpose immediately obvious and distinct from sibling tools like authenticate, report_bug, or marketplace.

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

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

No usage guidance is provided. The description does not state when to prefer this tool over alternatives, nor does it mention any exclusions or relevant context such as 'use this when troubleshooting version mismatches.'

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 about the specific information returned (MCPs, status, accounts, tool counts), which goes beyond the annotations without contradicting them.

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 front-loads the primary purpose and lists the key return elements. Every word earns its place, with no redundant or filler content.

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

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

Given the tool's simplicity (no parameters, no output schema), the description fully covers what the agent needs to know: it returns the current state with the specified details. It also implicitly differentiates from sibling tools by focusing on overall state rather than specific actions.

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 coverage is trivially 100%. With no parameters to document, the baseline score is 4; the description adds no parameter information because none is needed.

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 the current toolkit state, specifying exact components (installed MCPs, connection status, accounts, catalog tool counts). This distinguishes it well from sibling tools like authenticate, connect, or detran_rs_veiculo_consultar, which have different 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?

The description implies usage for checking toolkit state but does not explicitly state when to use it versus alternatives. No explicit guidance on when not to use it is provided, though the read-only nature and sibling context make it inferable.

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