DETRAN MG Taxa de Licenciamento: Emissão de DAE

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

It discloses useful behavioral details beyond the annotations: config-header auth gives a permanent non-expiring connection, while pasting a token gives session-only login, and calling with no arguments returns the login link. It does not describe failure behavior, but this is not contradictory with the idempotent/non-destructive 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 front-loaded with the core purpose and every sentence adds operational value. It is slightly dense and run-on, but remains appropriately sized for the login-token flow it explains.

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 one optional parameter and no output schema, the description adequately covers both invocation modes and expected outcomes (login link or session establishment). It could mention the exact response shape or error cases, but the essential usage context is 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. The description compensates fully by clarifying that token is a JWT/access token to be pasted, shows the exact invocation shape, and explains the behavior when no token is provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: authenticate an MCP.AI server for IDE agents via browser login and token capture. It also distinguishes the two operational paths (permanent config header vs session-only token), making the purpose and scope immediately understandable.

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 explicitly recommends the permanent config-header approach over session-only login and explains when to call with or without the token argument. It does not explicitly contrast this tool with sibling tools like 'connect', but the usage context and invocation modes are clear.

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

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

The description goes beyond the read-only and idempotent annotations by detailing the exact response structure (authenticated flag, pending array, connect_url) and conditional behavior. This provides transparency about what the tool returns without side effects.

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, using two clear sentences to convey the tool's behavior and both possible outcomes. It is well-structured and free of unnecessary details.

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

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

Given the lack of output schema, the description fully explains the expected return values in both scenarios. It is complete for the tool's simplicity and purpose.

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 confirms this (no properties). Since there are no parameters to explain, the description adds all necessary meaning trivially.

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 returns connection status and URLs, and it distinguishes the two possible outcomes (authenticated vs missing credentials). This makes the tool's purpose unambiguous and distinct from other tools like authenticate.

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 connection status but does not explicitly state when to use this tool over others (e.g., authenticate). It lacks guidance on prerequisite conditions or alternative scenarios, leaving some ambiguity.

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 state readOnly/idempotent/non-destructive. The description adds valuable extra context: pay-per-query cost, no platform credentials needed, public non-classified data, and LGPD responsibilities. This goes beyond the structured annotations and helps the agent anticipate practical behavior.

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

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

The purpose is front-loaded in the first sentence, followed by a compact paragraph of relevant context covering source, credentials, payment, and LGPD. It is somewhat dense but each clause contributes meaningful detail without 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?

The description is adequate at a broad level: it explains the general purpose, official source, cost model, and legal context. However, with no output schema it does not clarify the expected return value or highlighted DAE artifact, and input format is poorly detailed. These gaps make it minimum-viable rather than 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 schema lists renavam and ano as bare string types with 0% schema description coverage, and the description does not explain their meaning, format, or restrictions. The domain context gives partial hints, but an agent still lacks essential details like RENAVAM format and what the 'ano' refers to.

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 opens with the specific scope: DETRAN MG licensing fee DAE issuance and consultation from an official source. This clearly identifies the resource and operation, and immediately distinguishes it from the generic sibling tools like authenticate, marketplace, and 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?

The description gives contextual clues about when to use the tool — for official Brazilian DETRAN MG licensing data without platform credentials and with prepaid credits. However, it does not explicitly state when to use this tool versus alternatives or provide exclusion criteria.

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 works even when an MCP is not installed, returns connect/checkout links in specific conditions, writes require workspace owner/admin, and install makes MCPs permanent. This adds significant operational context far 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 information-dense, with each sentence adding substantive value. It front-loads the purpose and core flow. The single-paragraph structure is somewhat dense, but the content justifies the length; minor improvements could be clearer separation of marketplace vs prompt library.

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, multi-action tool with no output schema, the description covers the main scenarios: discovery, invocation, installation, billing, permissions, and prompt library. It lacks explicit details on error handling, response shapes for list_tools, and some action-specific parameters, but overall is complete enough for an agent to select and invoke the tool correctly.

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

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

With 23 parameters and 0% schema description coverage, the description carries the burden. It explains the central action parameter and core flow (search→describe→invoke) and mentions tool_id, arguments, mcp_id implicitly. However, several parameters (limit, immediate, conversation, prompt_* details) are not explained, leaving some semantics ambiguous for an agent.

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 is the official mcp.ai marketplace, a catalog and execution layer for MCPs/tools, plus a prompt library. It names the core actions (search, describe, invoke, install) and distinguishes them, making the tool's purpose and scope explicit.

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: 'prefer invoke for a single/occasional use', 'Use install only to make an MCP PERMANENT', and 'request_mcp asks us to build a NEW MCP when nothing fits'. It also contrasts with list_tools and explains the flow among search/describe/invoke, giving clear alternatives.

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

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

Annotations provide idempotentHint=true, destructiveHint=false, and readOnlyHint=false, which the description aligns with. The instruction to include a conversation array for reproduction adds useful context beyond annotations, though it doesn't detail side effects or expected responses.

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, purpose front-loaded, no redundant information. Every word adds value.

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 3-param tool with no output schema, the description covers the core use case and key param (conversation). It lacks details on response behavior, but given the low complexity, it's sufficiently 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?

Schema coverage is 0%, so the description must compensate. It explicitly mentions the conversation array, but does not clarify the purpose or format of 'message' (required) or 'context'. Partial compensation, leaving two of three params unexplained.

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?

Description clearly states the tool reports bugs, missing features, or feedback with a specific verb and resource. It distinguishes from sibling tools like authenticate, connect, 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?

Description implies when to use (for reporting bugs or feedback) but does not explicitly state when not to use or list alternatives. However, the distinct purpose among siblings is clear enough.

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, idempotentHint, and destructiveHint, covering the safety profile. The description adds that it shows 'current' versions, providing slight context, but does not describe return structure or behavior beyond that.

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 wasted words. It directly states the tool's purpose.

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?

This is a simple read-only tool with no parameters, strong annotations, and no unrelated complexity. The description sufficiently conveys what the tool returns and requires no additional context.

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 imposes no burden. The description does not need to explain parameter meaning, and the baseline for 0 params is 4.

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 ('Show') and a clear resource ('current MCP platform and adapter versions'). It is distinct from all sibling tools, which deal with authentication, connectivity, DAE consultation, marketplace, bug reporting, and 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 implies this tool is for checking version information, and siblings do not overlap. However, it does not explicitly state when to use it or mention any exclusions, so it falls short of providing explicit 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, fully covering the safety profile. The description adds what the returned state includes, but it doesn't disclose additional behavioral traits like latency, refresh behavior, or whether it triggers any connection checks. With annotations already strong, this is acceptable but not enhanced.

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?

One concise sentence that enumerates the key contents of the return value. No wasted words, clearly front-loaded with the main purpose.

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 is simple, has no parameters, and has minimal annotations, the description is sufficient. It covers all relevant aspects of what the tool returns. No output schema exists, but the description enumerates the return contents well.

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 100% (no properties). The description compensates by explaining what the status output includes, which is valuable since there is no output schema. Baseline for zero params is 4; the description adds context beyond the empty 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 returns the current toolkit state and specifies the contents (installed MCPs, connection status, accounts, catalog tool counts). It is specific and informative, but it does not explicitly distinguish from siblings, though no sibling performs the same function.

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

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

The description does not specify when to use this tool versus alternatives. It is a simple informational tool, so context is implied, but there is no explicit guidance on when it might be needed or when to prefer other tools.

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