Prefeitura SP São Paulo: Recursos de Multas

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

Adds context about the IDE agent audience and two invocation modes, but doesn't describe internal side effects like session creation or state changes. Annotations (readOnlyHint false, destructiveHint false) already cover the mutation profile, and the description doesn't contradict them, but it could clarify what happens after successful authentication.

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 slightly verbose but structured logically: explains the flow, presents two usage options, and specifies invocation. Every sentence contributes useful information; a bit of trimming could improve conciseness without losing 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 auth tool with a minimal schema and no output schema, the description covers the essential use cases and expected behavior. It could mention potential error conditions or return values, but it's adequate for the tool's 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?

Schema has a single optional 'token' parameter with no description (0% coverage). The description compensates by explaining the token is a JWT for session login and that calling with no args gets a link, adding 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 users for MCP.AI via browser login and token handling. It distinguishes itself from siblings like 'connect' by focusing on token-based authentication flow, with explicit verbs like 'log in' 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?

Provides explicit guidance on when to use the tool and how to invoke it: either with a token for session-only login or without args to obtain a link. It also recommends a config-based permanent approach, giving clear contextual usage instructions.

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 the tool read-only and idempotent, and the description adds behavioral specifics: it reveals that the tool outputs authenticated:true and empty pending[] when connected, and connect_url and per-install URLs when credentials are missing. This goes beyond the safety profile, providing concrete return semantics 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 two concise sentences, front-loaded with the main purpose and immediately providing the two distinct outcomes. Every sentence adds value without waste.

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 parameterless tool with no output schema, the description is sufficiently complete. It explains both possible return states, covering the main behavioral scenarios an agent needs to know. No additional context is necessary.

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 and fully covered. Per the rubric, a baseline of 4 is appropriate since there is nothing to explain; the description correctly omits irrelevant 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 the tool returns connection status and URLs, using a specific verb and resource. It differentiates from siblings like 'authenticate' by focusing on status checking rather than initiating authentication, making its purpose 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?

The description implies when to use the tool (to check connection status) by detailing the two possible outcomes (authenticated vs missing credentials). However, it does not explicitly compare to alternatives or state when not to use it, leaving some room for interpretation.

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?

Discloses many non-obvious behaviors beyond annotations: 'invoke works even when the MCP is NOT installed — it runs the tool pontualmente', 'returns a connect link' when credentials needed, 'returns a checkout/top-up link' when wallet empty, and 'Writes require workspace owner/admin.' Also notes search/describe flag installation status. 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 a single dense paragraph, but it follows a logical flow from purpose to core steps, key caveats, and prompt library. Every sentence adds value, but it lacks structural formatting like bullet points or sections. It is not excessively verbose, but readability could be improved without losing information.

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, no output schema, and 14 actions, the description covers the main flows thoroughly, including auth, billing, ownership restrictions, and prompt library. It explains most parameter group semantics. However, a few parameters like immediate, tier_slug, and request_details remain unexplained, and the return structure is not detailed. Still, it is far more complete than typical descriptions for such a complex 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 coverage is 0%, so the description carries the burden. It explains key parameters indirectly: action (all enum values), mcp_id, tool_id, arguments, prompt_* parameters for the prompt library. However, some parameters like limit, immediate, tier_slug, request_details, conversation are not explicitly described, though context may imply their use. Overall, it compensates well for the schema gap but not perfectly.

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 defines the tool as 'the official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It specifies a specific verb+resource (manage marketplace) and distinguishes it from siblings by describing its role in discovering, describing, and invoking MCPs, plus prompt library functions.

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 usage flow: 'Core flow: action=search... → describe... → invoke RUNS that tool.' It also gives clear when-to-use guidance: 'prefer invoke for a single/occasional use' vs. 'Use install only to make an MCP PERMANENT.' Mentions alternatives like list_tools and search_prompts for different intents.

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 discloses prepaid credit requirements, clarifies data is from official sources, non-confidential, and that the client is the data controller per LGPD. It also aligns with the readOnlyHint annotation. However, it does not detail potential side effects (though likely none) or the exact nature of the login parameters.

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 with repetitive mentions of official sources and LGPD compliance. It could be more streamlined, but it is not excessively long and the key points are still conveyed.

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 lacks essential details: parameter explanations, output schema (though none provided), and specific usage conditions. While it gives background on data origin and payment, it is insufficient for a developer to understand how to construct a valid request or interpret results.

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 parameter names and types but no descriptions. The description text does not explain the purpose of 'placa', 'renavam', 'login_cpf', 'login_cnpj', or 'login_senha', leaving their semantics entirely unaddressed.

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: consulting fine resources (recursos de multas) for São Paulo city. It is specific and distinct from the generic sibling tools (authenticate, connect, etc.), which are unrelated to fine queries.

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 some context (prepaid credit, hosted platform) but does not explicitly state when to use this tool versus alternatives. Sibling tools are system-level utilities, so usage is implied but not clearly delineated.

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 idempotentHint=true and destructiveHint=false, and the description adds context about including the conversation array for reproduction, which is a behavioral tip. There is no contradiction with the annotations. The description enriches the agent's understanding of how to provide useful data, which is especially helpful given the lack of parameter descriptions.

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 with no wasted words. The description is front-loaded with the primary purpose and adds one key usage hint in the second sentence. Perfectly sized for the task.

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 simplicity of the tool (no output schema, minimal parameters), the description covers the essential usage guidance. The mention of including the conversation array for reproduction is a valuable detail. It doesn't explain return values, but that's acceptable without an output schema. Could be slightly more explicit about the 'message' parameter's purpose, but overall complete enough.

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. While it mentions 'conversation array,' it does not clarify the meaning of 'message' (required) or 'context' parameters. The agent gets no guidance on formatting or purpose of these fields beyond the parameter names themselves. This leaves significant room for misinterpretation.

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.' This is a specific verb-resource pairing that distinguishes it from sibling tools like authenticate or show_version. The scope is well-defined 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?

The description implies when to use the tool (for reporting bugs/feedback) but does not explicitly compare to alternatives or mention exclusions. There is no guidance on when not to use it, but the purpose is straightforward enough that minimal additional direction is needed. This is adequate but not exemplary.

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 idempotentHint=true, and the description adds specificity by stating it reports both 'MCP platform and adapter versions'—clarifying the exact scope of output. This adds value beyond the structured 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, concise sentence that immediately states the tool's function. There is no extraneous information, and the purpose is front-loaded.

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 clear annotations, the description sufficiently explains what the tool does. It doesn't describe the return format, but without an output schema and with the tool's simple nature, this is adequate. Slightly more detail about what 'versions' includes could enhance completeness, but overall it is complete for the 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 per the rubric the baseline is 4. The description provides no parameter information, but none is needed since the schema is empty and the tool requires no input.

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 uses a specific verb ('Show') and resource ('MCP platform and adapter versions'), distinguishing it from sibling tools like authenticate or marketplace, which have entirely 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 retrieving version information but does not explicitly mention when to use it versus alternatives (e.g., toolkit_info, which might also provide version-related details). No exclusions or alternative hints are provided, though the unique purpose reduces 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, so the safety profile is well covered. The description adds useful context by specifying the exact state details returned (installed MCPs, connection 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 core purpose ('Returns the current toolkit state') and then enumerates the specific data elements. Every word adds value, 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?

Given the tool has no parameters, no output schema, and simple read-only behavior, the description fully covers the return contents. It clearly communicates what the agent will receive, making it complete for a tool of this complexity.

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 is empty, so parameter semantics are not applicable. Per the baseline for zero-parameter tools, a score of 4 is appropriate because the description carries no parameter burden.

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 explicitly identifies the resource ('current toolkit state'), enumerating concrete contents: installed MCPs, connection status, connected accounts, and count of catalog tools. This clearly distinguishes it from siblings like show_version 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 inspecting toolkit state but does not explicitly state when to use this tool versus alternatives such as show_version or connect. It provides clear context about what is returned, but no direct guidance on selection or exclusions.

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