SEFAZ AP: Certidão Negativa de Débitos

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

Annotations already indicate non-read-only, idempotent, non-destructive. Description adds valuable context about the browser flow, permanent vs session tokens, and the no-args link behavior. No contradictions.

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?

Three sentences with clear structure ('Best:' and 'Or'). Slightly verbose but every sentence provides necessary guidance.

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 auth tool without output schema, the description covers the full flow, both auth methods, and edge case (no args). Could mention error handling, but overall 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 has 0% description coverage, but the description fully explains the token parameter (JWT), its optionality, and the behavior when omitted. This goes well beyond the 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 this tool authenticates the user for MCP.AI in IDE agents, with specific actions (log in, copy token). It distinguishes from siblings like 'connect' by focusing on login/token handling.

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 clear guidance on two usage modes (permanent config vs session-only) and when to call with/without token. However, it does not explicitly address alternatives among sibling tools.

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 read-only, idempotent, non-destructive behavior, and the description adds valuable response-state details: authenticated:true with empty pending[], and connect_url plus per-install URLs for missing credentials. This gives the agent useful behavioral expectations beyond the safety flags 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 two concise sentences, front-loaded with the primary action and followed by conditional response behavior. Every word adds useful information with no redundancy or vague 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?

With no output schema and no parameters, the description sufficiently explains the return values for both the connected and missing-credential scenarios. This is enough for an agent to select the tool and understand the expected outcome before invoking it.

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 no parameter burden for the description to carry. The empty input schema and 100% schema coverage mean nothing further needs to be explained, matching the baseline for no-parameter tools.

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, with specific outputs for different states. It is distinct from sibling tools like authenticate by focusing on status/URL retrieval rather than initiating an authentication 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 provides clear contextual use cases: checking whether all providers are connected and obtaining connect_url when credentials are missing. It does not explicitly name alternatives or exclusions, but the purpose and triggering conditions are discernible.

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 the annotations, the description discloses important runtime behaviors: invoke works even when the MCP is not installed, it is one-off and does not bloat the tool list, credential/background paywall flows return connect/checkout links requiring user action then retry, and writes such as install/subscribe/cancel require workspace owner/admin. It also explains installed_in_toolkit vs installed_in_workspace flags. There is no contradiction with the stated 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 dense and front-loaded with the core identity and flow. It earns most of its sentences, but it runs into one long manual-style paragraph—space use, bullets, or short separations for billing, permissions, and prompt-library would have improved scanability.

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 very complex 23-parameter, no-output-schema marketplace tool, the description is impressively complete: it explains the core lifecycle, auth/payment side effects, prompt library, and other workspaces. It still leaves gaps: it does not enumerate every parameter, anything about `resume`, the `arguments` JSON format/system, or what the exact return shapes for overview outputs will look like, so the agent may still need to infer.

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 a strong burden here. It does add real meaning to core parameters: action values (search/describe/install/list_tools/subscribe/cancel/prompt actions), tool_id selection, prompt slug links, and cancel/billing behavior. However, several fields are left unexplained, such as limit, immediate, conversation, cancel_reason/comment, and arguments syntax; the description also refers to an 'invoke' flow that is not visible in the action enum, leaving some ambiguity.

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 a strong definition: 'official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It then clarifies the search–describe–invoke flow and distinguishes marketplace actions such as install, list_tools, subscribe, and the prompt library, so an agent can tell what this tool is for.

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 explicit decision guidance: 'prefer invoke for a single/occasional use', 'Use install only to make an MCP PERMANENT', and 'list_tools lists what is callable right now'. It also states the when-to-fallback path: 'request_mcp asks us to build a NEW MCP when nothing fits.' This is concrete when/when-not 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 the tool is not read-only, not destructive, and idempotent, so the description only needs to add behavioral nuance. It adds that the conversation array should be included for reproduction, but it does not explain whether data is transmitted externally, what happens after submission, or how the system treats duplicate reports.

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 short sentences, front-loaded with the purpose and followed by a useful reproduction instruction. Every clause earns its place with no 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 reporting tool with three flat parameters, no nested objects, and no output schema, the description is mostly complete. It gives the purpose and the key repro instruction, though explicit guidance for context and message would make it fully self-contained.

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 for all three params. Only the conversation parameter receives meaningful explanation, while the required message and optional context are left entirely to property-name inference without format or examples.

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 is specific: 'Report a bug, missing feature, or send feedback' names the exact action and resource. It also clarifies that the conversation array is intended for reproduction, distinguishing this from unrelated sibling tools like connect, marketplace, and the Sefaz lookup.

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 when-to-use context: call this when reporting a bug, missing feature, or feedback. It does not explicitly list alternatives, but none of the sibling tools serve the same reporting purpose, so no exclusion is necessary.

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 (readOnly, idempotent, non-destructive), the description adds meaningful behavioral details: it mentions prepaid credit cost, no platform credentials required, and LGPD compliance. This enhances transparency about side effects and data handling.

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 relatively short but contains redundancy (e.g., repeating 'consulta' and official source info). It could be more tightly structured, but it does not contain excessive verbosity.

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, the description should provide context about expected results or return data. It mentions the source and legal aspects but does not clarify what the tool actually returns (e.g., certificate status, JSON structure). It also fails to elaborate on the parameters, leaving the tool's overall usage incomplete.

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 defines three parameters (ie, cpf, cnpj) with no descriptions, and the tool description does not explain what they stand for or their expected formats. The description adds no value to parameter understanding, and schema coverage is 0%.

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 for consulting a negative debt certificate (Certidão Negativa de Débitos) from an official source. It specifies the action (consulta) and resource, distinguishing it from the sibling tools like authenticate or show_version.

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 context (official Brazilian tax query) and notes payment (prepaid credits) and lack of platform credentials, but does not explicitly state when to use this tool over alternatives or provide exclusionary conditions. The guidance is mostly implicit.

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 no destructive action. The description adds specificity about what versions are shown (platform and adapter), providing useful context beyond 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?

A single concise sentence with no fluff. It is front-loaded and every word 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 simple read-only tool with no parameters and no output schema, the description fully covers what the tool does. No additional explanation 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 no parameters, and schema coverage is 100%. Per rubric, baseline for 0 params is 4, and no param info 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 uses a specific verb 'Show' and a clear resource: current MCP platform and adapter versions. It distinguishes from sibling tools like connect, authenticate, and marketplace by specifying version information.

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 context is clear—use when you need version info. No exclusions or alternatives are mentioned, but given its simplicity and no parameters, the usage is self-evident.

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 detail beyond these by specifying the returned state elements (installed MCPs, connection status, accounts, tool counts). It adds enough behavioral context for an information-only tool without contradicting 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, well-constructed sentence that front-loads the action ('Returns the current toolkit state') and then compresses the relevant data items into one list. There is no filler or redundant repetition of the tool name.

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 zero-parameter, read-only state getter with strong annotations; the description covers the return value contents sufficiently and there is no output schema requiring additional explanation. It is a complete description for this tool's 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 input schema is empty, so there are no parameter semantics to describe; the zero-parameter baseline is 4. The description correctly implies that no configuration or arguments are needed to retrieve the toolkit state.

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') with a defined resource ('current toolkit state') and enumerates exactly what it provides: installed MCPs, connection statuses, connected accounts, and catalog tool counts. This clearly distinguishes it from action-oriented siblings like connect, authenticate, 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 makes the context obvious: this is the read-only way to inspect overall toolkit status, including MCP connections and accounts. It does not explicitly name show_version or provide when-not-to-use guidance, but the stated content makes the appropriate use case clear without being misleading.

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