Prefeitura SP São Paulo: Multas (Antiga)

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

The description discloses behavioral traits: it explains the token expiration (permanent vs. session-only), the requirement to log in via browser, and that calling with no args returns a link. Annotations already indicate idempotentHint=true, so the description adds context about the login flow and token handling, going 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 a single sentence but packed with essential information: the link, the token, the config option, and the session-only option. It front-loads the most critical info (log in browser, copy token) and provides the optional call syntax. It is slightly dense but every part 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?

Given the tool's simplicity (one optional parameter, no output schema), the description is quite complete: it explains the authentication flow, both usage modes (permanent and session), and the exact call syntax. However, it could benefit from clarifying what the link looks like or what the response is, but those are minor 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 schema has only one optional parameter 'token' with no description, and schema description coverage is 0%. The description compensates by explaining exactly how to use the 'token' parameter: 'call with { token: "<jwt>" } after the user pastes' and that no args gets the login link. This fully covers the parameter's meaning and usage.

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 server for IDE agents by logging in and obtaining an access token. It specifies the resource (this server's config or session-only login) and the action (authenticate), distinguishing it from sibling tools like 'connect' 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?

The description provides explicit when-to-use guidance: it gives two distinct usage scenarios (permanent config-based login vs. session-only paste), explains the best approach, and mentions the alternative of pasting the token. It also suggests the context (IDE agents like Cursor) and provides step-by-step actions (log in browser, copy token, add to config).

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, destructiveHint=false, covering safety. The description adds value by detailing state-dependent behavior (authenticated vs missing credentials) and the presence of 'pending[]', which hints at partial states. No contradiction found.

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, zero fluff, front-loaded with the action and result. Each clause adds meaningful information about conditions and outcomes.

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 status tool with no output schema, the description covers the main return values and conditions. It could detail what the URLs are for, but the core functionality is sufficiently explained for an agent to invoke and interpret the result.

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 baseline is 4. The description enriches understanding by explaining what the returned fields mean (authenticated, pending, connect_url), compensating for the lack of schema detail on outputs.

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 verb 'Returns' clearly indicates a read operation for connection status and URLs. It specifies the resource (connection status) and provides conditions for different outputs (authenticated:true vs connect_url), distinguishing itself from siblings like 'authenticate' which perform actions.

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 explains when different outputs occur ('When all providers are connected', 'When credentials are missing') but does not explicitly state when to use this tool versus alternatives like 'authenticate'. It implies this is for checking status, but lacks explicit conditions or 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 are thin (readOnlyHint:false, openWorldHint:true, destructiveHint:false), so the description bears the transparency burden and delivers richly: one-off invoke 'runs the tool pontualmente... without bloating the tool list,' auth-needing MCPs 'return a connect link,' paid-with-empty-wallet returns 'a checkout/top-up link,' and 'Writes... require workspace owner/admin.' No contradiction with annotations—the write-capability implied by readOnlyHint:false is consistent with install/uninstall/invoke.

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?

Every sentence carries distinct informational weight—core flow, one-off invoke, install persistence, subordinate actions, permission requirements, and the prompt library. However, ~300 words are packed into a single unbroken paragraph; breaking out the prompt-library section or the action catalog would aid scannability. Dense but not bloated.

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 14 actions, 23 parameters, no output schema, and minimal annotations, this description is remarkably complete: it covers the search→describe→invoke pipeline, offline invoke behavior, auth and billing fallback flows, install-vs-invoke tradeoffs, permission tiers, and the whole prompt-library subsystem including output hints ('returns a shareable mcp.ai/p/<slug> link'). Minor gaps (the 'resume' action and 'immediate' param go undescribed) but these are trivial against the full surface area handled.

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, and it does substantially: it maps action→parameter usage (query for search, mcp_id for describe, tool_id+arguments for invoke, prompt_slug/prompt_vars for get_prompt). However, it leaves roughly half the 23 parameters unexplained (immediate, tier_slug, conversation, cancel_reason, request_details, prompt_targets). Strong compensation but not exhaustive given the coverage 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 opening line defines the tool precisely: 'The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It then states the core search→describe→invoke flow with specific verbs, and distinguishes the tool's sub-actions from its siblings (authenticate, connect, toolkit_info). For a 14-action tool, the purpose statement is unambiguous and grounded.

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 explicit when-to-use guidance with named alternatives: 'prefer invoke for a single/occasional use' vs 'Use install only to make an MCP PERMANENT'; 'subscribe/cancel handle per-MCP billing; report_bug sends feedback; request_mcp asks us to build a NEW MCP when nothing fits.' It even warns when invoke returns a connect/checkout link and instructs 'the user opens it, then you retry.' No score could reflect this better.

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=true, idempotentHint=true, destructiveHint=false, and the description adds that this is a paid consultation requiring prepaid credits and that data is not confidential. It also notes LGPD responsibilities, which is useful context. However, it doesn't mention potential rate limits, response delays, or that the data might be stale, but with annotations covering safety, the description adds reasonable 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 a single paragraph that front-loads the purpose, but includes several clauses about hosting, payments, and LGPD that could be condensed. It is not overly long, but it lacks clear structure (e.g., separate sections for usage and behavior).

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 requires 6 parameters with no output schema and no parameter documentation, the description is incomplete for effective use. It provides some context on payment and legal aspects, but misses crucial details like parameter formatting, error handling, and what the result contains. The annotations help, but the complexity of a paid consultation with multiple login options demands more.

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%, and the description does not explain any parameter formats or purposes beyond stating it requires login credentials and vehicle plate/RENAVAM. The 'login_senha' is required, but there is no detail on how login_cpf vs login_cnpj are used or the format of 'placa' and 'renavam'. The parameter 'ignora_boleto' is entirely unexplained. The description must compensate for the 0% coverage but does not.

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 this tool queries fines (multas) from São Paulo's official source, specifying it's for 'Antiga' (old) fines and that it's an official consultation. It distinguishes itself from siblings by focusing on this specific domain, though it doesn't explicitly compare to other fine-related tools.

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 mentions payment via prepaid credits and that it's hosted by the platform, but provides no guidance on when to use this tool versus alternatives (e.g., other fine query tools, newer versions). It also doesn't specify prerequisites beyond credentials, and the mention of 'sem credenciais da plataforma' is confusing without elaboration.

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 annotations by explaining the conversation array's role in reproduction. However, it does not disclose any side effects, authentication needs, or what happens after the report is submitted. Annotations indicate it is not read-only and not destructive, but the description adds limited extra behavioral 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 only two sentences, with the primary purpose stated first and the specific reproduction requirement second. Every sentence earns its place with no redundancy or filler.

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 bug-report tool with no output schema, the description covers the core purpose and the necessary input guidance. It does not mention return values or prerequisites, but the low complexity and annotations make the description sufficiently complete for most uses.

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 must compensate. It explicitly explains the 'conversation' parameter as an array for reproduction and implies 'message' is the report text. The 'context' parameter is not explained, leaving a gap in full parameter understanding.

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 'Report a bug, missing feature, or send feedback' with a specific verb and resource. It distinguishes the tool from sibling tools, none of which handle bug reporting, and adds the reproduction detail about the conversation array.

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 intended usage is clear from the purpose: use when reporting a bug, missing feature, or feedback. It does not explicitly state alternatives, but no sibling tool offers similar functionality, so the context is adequately conveyed.

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 as true, covering the safety profile. The description adds the specific content (platform and adapter versions), which is sufficient for a simple read operation. No contradictions or missing critical behavioral details.

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, front-loaded with the verb and resource, contains zero wasted words. It is perfectly concise and well-structured.

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 version tool with no output schema, the description fully explains its purpose and behavior. No additional context is needed.

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 schema description coverage is trivially 100%. The description doesn't need to explain parameters; the baseline of 4 applies because there is nothing to compensate for.

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 clearly identifies the resource ('current MCP platform and adapter versions'). This distinguishes it from sibling tools like authenticate, connect, and marketplace, which have distinct 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?

While no explicit when/when-not guidance is given, the tool's purpose is self-evident as a read-only version check. No alternatives exist among siblings, so context is implied. It clearly conveys this is for retrieving version information without any 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds specific details about the returned data (installed MCPs, connection status, accounts, catalog tools) without contradicting annotations. It does not mention potential latency or side effects, but for a read-only info tool, this is sufficient.

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, focused sentence that lists exactly what is returned. No filler or redundant information, achieving maximum 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 zero-parameter read-only tool with rich annotations, the description fully covers what the agent needs to know: what it returns and that it's safe to call. The absence of an output schema is mitigated by the explicit list of returned fields.

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 there is nothing to document. The baseline for 0 parameters is 4, as the description need not add parameter details.

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 it returns toolkit state with specific contents: installed MCPs, connection status, accounts, and catalog tool counts. It uses a specific verb ('Returns') and resource ('toolkit state'), and distinguishes itself from sibling action tools like connect and 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 the current toolkit configuration and status, which is distinct from sibling tools that perform actions. It does not explicitly state when not to use it or name alternatives, but the context makes it clear this is an introspection tool.

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