Prefeitura SP Guarulhos: Certidão Negativa de Débitos Mobiliários

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

Annotations indicate idempotentHint=true and readOnlyHint=false, but the description adds crucial context: calling with no args returns a link (likely opening a browser flow), calling with token establishes a session, and there's a permanent alternative via config. It clearly states the token is a JWT and that the session-only login is not permanent. However, it doesn't explicitly mention side effects like whether the token is stored or how the session is maintained, but with idempotentHint=true and destructiveHint=false, the risk is low.

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, front-loaded with the purpose, and every sentence adds value: it explains the browser flow, the permanent config option, and the two invocation modes. No redundant information; it fits in a few lines and is immediately actionable.

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 complexity (authentication with multiple modes) and lack of output schema, the description is remarkably complete. It covers the main usage scenarios, prerequisites (user pasting token), and alternatives. It even mentions the recommended permanent setup, which is beyond the typical tool description. No critical gaps spotted.

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?

Although schema description coverage is 0%, the description provides meaning for the single 'token' parameter: it should be a JWT pasted by the user, and omitting it triggers a different behavior (get link). This compensates for the lack of schema documentation. The parameter semantics are clear enough for an agent to decide when to include it.

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: to authenticate by providing a browser link, accepting a token for session-only login, or recommending a permanent config-based solution. It distinguishes itself from sibling tools by focusing on authentication, which is unique among the listed siblings (connect, marketplace, 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 explicit when-to-use guidance: call with no args to get the login link, call with token after the user pastes a JWT, or alternatively configure a permanent header in the server's config. It clearly contrasts the session-only vs permanent options, which is essential for an agent deciding how to invoke this tool.

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 context about return states (authenticated:true, pending[], connect_url) but doesn't disclose additional behaviors like rate limits or error handling. No contradiction.

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, concise and front-loaded with the core purpose. Every sentence adds useful information about return states.

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 zero parameters, strong annotations, and no output schema, the description sufficiently explains the tool's behavior and return states. It could mention whether output is a JSON object but that is implied by 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?

Tool has zero parameters, so schema coverage is trivially 100%. Description adds value by explaining what the output means (authenticated, pending, connect_url), even though there are no parameters to document.

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, specifying what happens when providers are connected or credentials are missing. It distinguishes from siblings like 'authenticate' which likely initiates the connection flow.

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 it versus 'authenticate' or other tools. It lacks explicit alternatives 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?

The description goes well beyond the annotations, disclosing that invoke runs tools one-off without installing them, that credential/payment gaps surface as links, that install/subscribe/cancel require owner/admin, and that prompt-library links open without login. This gives the agent a strong model of side effects and prerequisites.

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 dense but front-loaded with the core purpose and flow, then covers each action succinctly. It is somewhat long and packs marketplace behavior and the prompt library into one paragraph, but almost every sentence adds needed information for a tool with this breadth.

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 complexity — 14 actions, 23 parameters, and no output schema — the description does a strong job covering the main flows, auth requirements, billing/payment behavior, installed-status flags, and the prompt library. It does not describe return shapes or error behavior for every action, but provides enough guidance for an agent to select and invoke the correct action.

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 must compensate, and it does explain the core action flow and key roles like action, mcp_id, tool_id, and arguments. However, many parameters such as limit, immediate, tier_slug, conversation, cancel_reason, request_name, and report_context are never explicitly mapped, leaving gaps for correct invocation.

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 is the official mcp.ai marketplace catalog and runtime, with a specific multi-action design. It differentiates the tool from siblings by enumerating its actions and the overall core flow.

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 explains when to use each action: search vs describe vs invoke, prefer invoke for one-off use, use install only for permanent additions, list_tools for currently callable tools, and report_bug/request_mcp for feedback/new MCPs. It also provides clear be-havioral guidance on when invoke requires a connect or checkout link and to retry afterward.

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, and the description is consistent with this read-only nature. The description adds meaningful context beyond annotations: no platform credentials required, prepaid per-query pricing model, non-confidential data provenance, and LGPD data controller responsibilities. This gives an agent a realistic understanding of side effects (payment deduction) and compliance expectations.

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?

Four information-dense sentences in a single paragraph, front-loaded with the core purpose before adding payment, data provenance, and compliance details. No filler words. It could be slightly restructured so the LGPD sentence is more integrated, but overall every clause 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?

For a query tool with 3 optional parameters and no output schema, the description should clarify what the response looks like (e.g., certificate PDF, JSON with debt status, error on found debts) and whether any identifier is required. The description covers 'where,' 'how,' and 'legal what' but omits the critical 'what do I get back' since neither the schema nor annotations provide this 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?

Schema coverage is 0% and the description provides zero additional detail about the three parameters (cpf, cnpj, inscricao_municipal). It doesn't clarify that at least one is likely needed, whether they're mutually exclusive, format requirements (with/without punctuation), or which to provide for which use case. The parameter names are self-evident to Brazilian audiences but an AI agent would benefit from constraints like 'provide at least one' or 'inscricao_municipal applies only to Guarulhos-registered entities.'

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 purpose: querying a 'Certidão Negativa de Débitos Mobiliários' (Negative Certificate of Municipal Debts) from Prefeitura SP Guarulhos via official sources. The verb 'consulta' (query) and resource 'CNDM' are specific and identifiable. It's not a tautology, though it could more explicitly differentiate from potential sibling data 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 provides context about when to use the tool: it's a pay-per-query service using prepaid credit, hosted on the platform, and queries public/official data available to citizens. However, it doesn't explicitly state when NOT to use it or name alternatives beyond the payment model. The LGPD note about the client being the data controller is a useful decision factor but isn't a substitute for explicit 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?

The description adds context beyond the `readOnlyHint` and `idempotentHint` annotations by informing the agent of the expected side effect (sending data) and the guarantee that including the conversation array aids in reproduction. While it doesn't detail the entire external process, it clarifies the user-request context. The annotation `idempotentHint: true` isn't contradicted; it's a benign report, so the description is acceptable.

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 compact two sentences. The first sentence clearly states the function, and the second gives a specific, actionable instruction for the user to improve the report. There is no wasted language.

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 no output schema and moderate complexity (one required param), the description adequately covers the core use case, expected input, and a key behavioral requirement for reproduction. It could be slightly more explicit about the behavior when no conversation is provided, but it's already sufficient for an agent to use 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?

The description successfully compensates for the 0% schema coverage by explaining the `conversation` field ('Include the conversation array with recent messages for reproduction'). It clarifies why fields like `message` are needed. While `context` is not explicitly detailed, the description provides enough semantic context for the overall tool function.

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 uses a specific verb (report/send) and a clear resource (bug/feature/feedback) and explicitly scopes the tool's function, distinguishing it from siblings like marketplace 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 clearly implies when to use this tool (whenever a bug or feature request is mentioned) and provides explicit guidance on how to use it effectively by instructing to include the conversation array for reproduction. It doesn't explicitly name alternative tools, but the use case is well-defined and distinct from its siblings.

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 'current' versions, implying a real-time lookup, but does not detail output format or potential network calls. With annotations doing the heavy lifting, this is adequate but not rich.

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, clear sentence with no filler. It is front-loaded and precisely states 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?

Given the simplicity of the tool, no output schema is required. The description fully conveys the output (versions) and is complete for the intended use.

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 trivially covers 100% of the parameter space. No parameter descriptions are needed, but the description is sufficient as the purpose clarifies what information is returned.

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 resource ('current MCP platform and adapter versions'), clearly defining the tool's purpose. It is distinct from siblings like 'authenticate' or 'connect', which have different 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?

While no explicit when-to-use or alternatives are given, the context is simple and self-evident. The tool is for retrieving version info, and no competing tools are apparent. This clarity suffices for a trivial read-only operation.

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. The description adds valuable context by enumerating what the state snapshot includes, which goes beyond the structured metadata and helps the agent know what to expect.

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 that states the operation and immediately lists the concrete return categories. Every part earns its place, with no filler or 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, no-argument, read-only tool with strong annotations, the description fully covers what the agent needs to know: it describes the output dimensions (MCPs, connection status, accounts, catalog tool counts). No output schema exists, but the description compensates adequately.

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?

This tool has zero parameters and the schema coverage is effectively 100% (empty properties object). There is no parameter-information burden for the description to carry, so the baseline 4 for zero-parameter tools applies.

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 clearly identifies the resource ('current toolkit state') and the exact returned content: installed MCPs, connection status, connected accounts, and catalog tool counts. This distinguishes it from sibling tools like authenticate/connect, which imply state-changing operations.

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 establishes a clear informational context: use this to inspect the toolkit's current state. It does not explicitly name alternatives or exclusions, but the usage context is obvious and distinct from sibling tools.

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