SEFAZ AL: Certidão Negativa de Débitos

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

Even with annotations indicating readOnlyHint=false and idempotentHint=true, the description adds valuable context: it discloses the browser login flow, user interaction, permanence of config-based vs session-only, and token format (JWT). No contradiction 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 slightly long but each sentence adds value, front-loading the purpose and then detailing options. It's structured with a 'best' recommendation and an alternative, making it easy to parse.

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 moderate complexity (authentication flow, two modes, one optional param) and no output schema, the description covers essential use cases, configuration vs session, and token details. It is complete enough for an agent to invoke 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 schema has zero description coverage, but the description explains the token parameter (JWT for session-only) and its usage, plus the no-args behavior. This compensates well for 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: to authenticate with MCP.AI. It specifies the resource (authentication) and distinguishes between two modes (permanent config-based vs session-only), which differentiates it from sibling tools like 'connect' that 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?

Provides explicit when and how to use: log in via browser, copy token, and either add to config for permanent access or paste for session use. It also mentions calling with no args to get the link. Though it doesn't explicitly state when NOT to use, the alternatives 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?

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful conditional semantics: authenticated:true with empty pending[] when all providers are connected, and connect_url when credentials are missing. No contradiction 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?

Two sentences with dense, useful information. The opening sentence states the core purpose, and the second sentence explains the two relevant outcome branches. No filler or redundant wording.

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 low complexity (no parameters, no output schema, and clear annotations), the description covers the main return behaviors well. It could go slightly deeper into the full set of states, but for this tool the provided information is largely sufficient.

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 parameter semantics are inherently minimal. The description still helps by explaining the meaning of the result under different credential states, which is appropriate for a parameter-less tool.

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 names the resource clearly ('connection status and URLs'). It immediately signals what the tool does and the conditional authenticated/pending states help distinguish it from related 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 gives clear context for when calling this tool is meaningful: check connection status, and if credentials are missing, obtain connect URLs. It does not explicitly name alternatives or exclusion criteria, but the intent is evident from the conditional behavior described.

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 key behavioral details: invoke works even for non-installed MCPs, returns connect/checkout links when needed, installs add tools permanently, writes require owner/admin, and search/describe mark installed indicator. These details go beyond annotations and align with them, so there is 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?

The description is front-loaded with the core definition and valuable flow. It is long but every sentence adds value. It would benefit from a structure with bullets or sections because it blurs MCP catalog, invoke, install/subscribe, and prompt-library into one large paragraph.

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 size and complexity of the tool, the description is unusually complete: it says when to use invoke vs install, lists both auth and payment side-effects, explains installed flags, and describes the prompt library portions. It still has gaps around many input parameters and generic fallback behaviors, but no output schema and no per-property descriptions make those gaps harder to resolve.

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 23 parameters at 0% description coverage, so the text carries a lot of the burden. It does explain the main action values and core variables such as tool_id, prompt_slug, prompt variables, and cancel reasons. However, parameters like immediate, tier_slug, conversation, request_details, prompt_targets, and many optional fields still have no meaningful description, leaving gaps.

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 a strong identity and resource: the official mcp.ai marketplace and the way to search, describe, and run MCP tools. It differentiates itself from sibling tools by walking through the catalog flow and describing each action family correctly.

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?

Explicit usage guidance is present: use install only for making an MCP permanent, prefer invoke for one-off use, list_tools for current callable tools, subscribe/cancel for billing, report_bug for feedback, and request_mcp when nothing fits. This clearly distinguishes the main workflow from alternative tools/actions.

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 disclose that the tool is idempotent (idempotentHint: true) and non-destructive (destructiveHint: false). The description adds the context that the conversation array is needed for reproduction, which gives a hint about how the tool behaves (collects context). However, it does not describe side effects, response handling, or other behavioral details. With annotations present, the bar is lower, and the description adds some value, so a 3 is appropriate.

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: two sentences that front-load the purpose and then provide a key usage detail. Every word earns its place. There is no fluff or repetition. It is appropriately structured for a simple tool.

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 tool is simple (3 params, no output schema, no nested objects), and the description covers the essential aspects: what it does and a key parameter. It does not explain return values but no output schema exists. It could mention that it sends a report and maybe the result, but for a low-complexity tool the description is adequate. Minor gaps like not mentioning that 'message' is required or what 'context' is keep it from being a 5.

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 no parameter descriptions (coverage 0%), so the description must compensate. It explicitly explains the 'conversation' parameter ('Include the conversation array with recent messages for reproduction.') but does not elaborate on 'message' or 'context'. 'message' is fairly obvious from the purpose, but not explicitly stated. Overall, it adds some semantic meaning but is incomplete, warranting a 3.

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.' It uses specific verbs and a resource (bug report/feedback), and it is distinct from sibling tools which are unrelated (auth, connection, marketplace, etc.). The intended action is 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 provides a concrete usage instruction: 'Include the conversation array with recent messages for reproduction.' This indicates when and how to use the tool. However, it does not explicitly mention when not to use it or alternative tools, but given the sibling tools are unrelated, it is relatively clear. It lacks explicit exclusions but is adequate for context.

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 readOnly, idempotent, and non-destructive behavior. The description adds meaningful context beyond annotations: no platform credentials, pay-per-query billing, non-confidential data, and LGPD controller responsibility. 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?

The description is compact and front-loaded with the main purpose. The additional sentences about payment, official data, and LGPD are relevant and not excessive.

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 tool is simple and covered by strong annotations, but with no output schema and no parameter documentation the description should at least clarify that CPF/CNPJ identifies the taxpayer and what the returned certificate is. It is adequate but leaves those operational details implicit.

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 0% description coverage and the tool description does not mention CPF/CNPJ, whether either is required, or how to choose between them. The names are self-explanatory, but the description does not compensate for the low schema coverage.

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 explicitly names the resource (SEFAZ AL Certidão Negativa de Débitos) and the action (consulta em fonte oficial). It is specific enough to distinguish this tool from the unrelated sibling 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?

It explains when the tool is appropriate: querying official Brazilian sources, using prepaid credits, and without platform credentials. It does not explicitly state when not to use it, but there are no obvious alternative 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 cover safety (readOnlyHint, idempotentHint, destructiveHint). The description adds no additional behavioral context such as side effects, authentication needs, or return format, providing no value 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?

One sentence, concise, and front-loaded with the action. No unnecessary words 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?

While the tool is simple and the description covers the purpose, it does not describe the return format or expected output. Since there is no output schema, this leaves some ambiguity, but for a version query it may be acceptable.

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 describe. Per the rubric, 0 params gets a baseline of 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?

Description clearly states the tool shows current MCP platform and adapter versions, using a specific verb ('show') and resource. It is distinct from sibling tools like authenticate or report_bug, which have 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 does not provide explicit guidance on when to use or not use this tool, nor does it mention alternatives. However, the purpose is self-evident, so usage is implied but not explicitly stated.

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 readOnly, idempotent, and non-destructive behavior. The description adds detail on what data is returned, which is transparent. 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?

One concise sentence that lists all relevant output aspects without redundancy. No unnecessary words.

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 no output schema, the description fully explains what the tool returns. It is complete for understanding the tool's functionality and does not require 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 input schema has zero parameters, and the description correctly explains the tool's purpose without needing to clarify inputs. Full coverage by 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?

Clearly states it returns the current toolkit state, listing specific information (installed MCPs, connection status, accounts, catalog tool counts). This distinguishes it from sibling tools like show_version or sefaz_al_certidao_debitos_consultar.

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 it versus alternatives. However, given the read-only informational nature, it is reasonably clear for typical use cases.

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