Cadastro PJ Plus

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

The description adds context beyond annotations by explaining that calling with no args returns a link, while passing a token enables session-only login. It also differentiates between a permanent (header config) and session-based connection. No contradiction with annotations (readOnlyHint=false, idempotentHint=true, destructiveHint=false) exists, as the described actions are idempotent and non-destructive.

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 efficiently cover the context, best practice, and session alternative. Each sentence serves a distinct purpose, and the message is front-loaded with the tool's target audience and core action. No unnecessary repetition 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 one-parameter tool, the description covers the primary usage modes and token semantics. However, it does not explicitly specify return values or error conditions (e.g., what happens on successful auth or invalid token), which would improve completeness given there is no output schema.

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 only defines 'token' as a string with no description, so the description carries full responsibility. It does this thoroughly by explaining the token is a JWT, that it is optional, and that omitting it generates a login link. It compensates perfectly for the 0% 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 clearly states the tool's purpose: authentication for IDE agents by logging in, copying a token, and either configuring it as a header or passing it for a session. It distinguishes from siblings like 'connect' by focusing specifically on the authentication workflow, not general connection setup.

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 provides explicit instructions on when to use each mode: 'Best: add it to this server's config as a header...' for permanent access, and 'Or paste it here for a session-only login' with the token parameter for temporary access. It does not explicitly mention alternatives or exclusions relative to sibling tools, but the guidance within the tool is clear and practical.

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: no credentials needed, prepaid per-query cost, access to public official data, and LGPD compliance. It also clarifies the client's responsibility as data controller. Nothing contradicts the annotations, and the added legal/billing context goes beyond structured fields.

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 focused four-sentence paragraph that front-loads the core purpose, then adds billing and legal context. It is not overly verbose, though some content (LGPD clause) could be considered tangential for an AI agent. Overall, it is efficiently structured and 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 2-parameter read-only tool with no output schema, the description covers the purpose, authentication, cost, data source (public official bases), and legal obligations. It does not describe return value format or pagination, but given the tool's simplicity and presence of readOnlyHint, the description is sufficiently complete for selection and invocation. Sibling tools offer no alternative, so no comparison 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?

Schema description coverage is 0%, so the description must compensate for explaining parameters. It does not mention either 'CNPJ' or 'completo' explicitly. The CNPJ parameter is self-explanatory from the tool name and description, but the 'completo' boolean is ambiguous—whether it controls the depth of data returned is not stated. The description says 'além dos dados básicos' which hints at a 'complete' mode, but this is not explicit. Thus, the description adds little parameter-level meaning.

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 states the tool returns "Dados cadastrais ampliados de uma empresa a partir do CNPJ, além dos dados básicos," clearly specifying the resource (CNPJ) and the scope (expanded corporate data). It distinguishes itself from the unrelated siblings (authenticate, connect, etc.) and the verb is implicit in the tool name '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 provides clear context on how to use the tool: it is hosted by the platform, requires no credentials, and charges per query with prepaid credit. It also notes the data is public and LGPD applies, but it does not explicitly discuss alternative tools or when not to use it. Given the sibling tools are unrelated, this is acceptable and achieves a clear context without explicit 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 already declare readOnlyHint and idempotentHint, covering safety. The description adds behavioral detail about the response shape in different states. However, it does not describe partial connectivity scenarios, leaving a small gap.

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, front-loaded with the core purpose, then conditional details. No wasted words, and the structure is easy to scan.

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 no-parameter status tool with rich annotations, the description covers the main use cases well. It explains both fully-connected and missing-credentials states, though it leaves partial connectivity ambiguous.

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 input schema is empty. The description doesn't need to cover parameter details, and the schema coverage is 100% vacuously. Baseline 4 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 clearly states the tool returns connection status and URLs, with specific detail on two outcomes. This is a distinct verb+resource that differentiates it from siblings like 'authenticate' which initiates connections.

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 on when the tool is useful by describing the two scenarios (all connected vs. missing credentials). It implies the user should call this to check connection state, though it does not explicitly mention 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 far beyond annotations by disclosing critical behaviors: invoke runs one-off even without installation, returns connect/checkout links for missing credentials/wallet, and writes (install/uninstall/subscribe/cancel) require workspace owner/admin. It also explains the prompt library's accessible-without-login property. This is rich behavioral context that annotations alone do not provide.

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 long (~220 words) but every sentence packs essential information for a tool with 14 distinct actions. It is front-loaded with the core purpose and flow, uses clear action->outcome arrows, and avoids fluff. It could be slightly better organized, but the density is justified for the complexity.

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 of this complexity with no output schema and zero schema parameter descriptions, the description covers almost every relevant aspect: core flow, one-off execution, auth/billing edge cases, permission requirements, permanent vs occasional usage, prompt library, and even cross-tool distinctions (list_tools vs describe). It is exceptionally complete for its scope.

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 compensates well by explaining the primary parameters: action=search/describe/invoke, mcp_id, tool_id, arguments, and prompt-related params (prompt_slug, prompt_vars, prompt_body). However, many of the 23 params (e.g., limit, query, immediate, tier_slug, conversation) are not individually addressed, leaving some gaps. It earns a 4 for the significant semantic coverage it does provide relative to 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 the tool's role as the official mcp.ai marketplace catalog and execution engine. It immediately distinguishes its core flow (search → describe → invoke) and lists the main action categories, making it unmistakable what this tool does and how it differs from simple utility 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 gives explicit when-to-use guidance, including a direct comparison: 'Use install only to make an MCP PERMANENT... prefer invoke for a single/occasional use.' It also maps the core flow (search->describe->invoke), explains when list_tools is appropriate, and mentions alternatives like report_bug and request_mcp. This is exemplary usage 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 idempotentHint=true and destructiveHint=false. The description adds useful context that this is a feedback mechanism and that the conversation array aids reproduction. 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 a single sentence that front-loads purpose and includes a necessary usage hint. 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?

The tool is simple and has no output schema, but the description omits details about the conversation format (JSON string?), the meaning of context, and expected side effects. It's adequate for basic invocation but leaves 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?

Schema description coverage is 0%, so the description must compensate. It only mentions the conversation array, and even that is imprecise—the schema defines conversation as a string not an array. The required message and optional context parameters are not explained at all.

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 'Report' with clear objects: bug, missing feature, or feedback. This clearly distinguishes from sibling tools like authenticate, show_version, etc., which 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?

The description implies when to use (when there is a bug, missing feature, or feedback) and provides an explicit instruction to include the conversation array for reproduction. While it doesn't name alternatives or exclusions, the tool is unique among siblings, making the context 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that it returns 'current' versions, which specifies the output context but does not disclose additional behavioral traits such as return format or potential side effects. Given the low bar set by strong annotations, this is acceptable but not above average.

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, front-loaded with the action and resource. It contains no filler or redundant information, earning every word.

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 extremely simple with no parameters and no output schema. The description fully conveys what the tool does and what it returns ('current MCP platform and adapter versions'), covering the necessary context. Annotations fill in the safety and side-effect details, making the description 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?

There are zero parameters, so the schema is trivially complete (100% coverage). The description does not need to compensate for parameter information. The baseline for 0 params is 4, and the description appropriately omits any parameter details since there are none.

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: 'Show the current MCP platform and adapter versions.' It uses a specific verb ('Show') and resource (platform/adapter versions), but does not explicitly distinguish it from sibling tools like toolkit_info, which might also report version information. Thus it is clear but lacks explicit differentiation.

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—when you need to know the current versions of the MCP platform and adapter—but it does not provide any explicit guidance on when to use this tool over alternatives, nor does it mention any context where this tool is inappropriate. The usage is implied by the resource, so it meets the 'implied usage' criterion.

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 provide readOnlyHint=true and idempotentHint=true, so the safe-read behavior is known. The description adds context by enumerating the exact items returned, which is helpful given the absence of an output schema. It does not contradict annotations, nor does it discuss side effects, which is appropriate for a read-only tool.

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 that front-loads the return action and lists the key categories of information. No wasted 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?

Despite lacking an output schema, the description sufficiently describes the return content for an agent to anticipate the response structure. It covers all relevant aspects for a zero-parameter read-only info 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?

The tool has zero parameters, so schema coverage is complete (100%). The description does not need to add parameter semantics; it simply clarifies that the tool takes no input, which aligns with the empty 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 uses the specific verb 'Returns' and clearly defines the resource as 'current toolkit state,' listing the exact components (installed MCPs, connection status, connected accounts, catalog tool counts). This distinguishes it from siblings like `show_version` and `connect`, which focus on different aspects.

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 communicates the tool's purpose as a state inspector, so an agent can infer when to use it (e.g., checking connectivity before operations). However, it does not explicitly name alternative tools or when-not-to-use conditions, so it falls short of a fully explicit guideline.

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