Dados Cadastrais PF (Argentina)

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

With annotations indicating idempotent and non-destructive, the description adds useful behavioral details: returning a link when no args, using a JWT token for session-only login, and non-expiring header configuration. 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?

The description is front-loaded with the purpose, then explains methods in a structured manner. A bit long but each sentence adds 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 authentication tool with one optional parameter and no output schema, the description covers usage modes and token semantics sufficiently. Missing some details like response format, but not critical.

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 fully explains the optional 'token' parameter: it is a JWT for session-only login, and omitting it returns a link. This compensates 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 identifies the tool's purpose: authentication for IDE agents. It explains the login flow and token handling, but does not explicitly distinguish from sibling tool '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?

Provides clear context for when to use (for IDE agents needing auth) and gives two usage modes: permanent via header config or session-only via token. Does not mention alternatives or when-not-to-use.

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 valuable behavioral detail by specifying the response conditions (authenticated:true with empty pending[] vs. connect_url for toolkit and per-install URLs), which goes beyond what annotations 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 two sentences with no wasted words. The first sentence front-loads the main purpose, and the second expands on specific return behavior. 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?

Given that the tool has no parameters, no output schema, and is a simple status check, the description fully covers the tool's behavior in both relevant scenarios (all connected vs. missing credentials). It is complete and self-contained, even without an 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 tool has zero parameters, so there are no parameter semantics to explain. According to the rubric, a baseline of 4 is appropriate for 0-param tools, and the description correctly avoids inventing unneeded 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, which is a specific and distinct purpose compared to sibling tools like 'authenticate'. It explicitly describes the resource (connection status) and the action (returns), making it clear this is a read-only status check.

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 by explaining what happens when providers are connected versus when credentials are missing, implying this tool is used to check connection status before or after authentication. It doesn't explicitly say 'use this instead of authenticate', but the behavior described makes the usage scenario 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?

The description goes well beyond the minimal annotations by explaining key behavioral traits: invoke runs a tool one-off without adding it to the toolkit, returns a connect link when credentials are needed, returns a checkout/top-up link when payment is required, and that writes require workspace owner/admin. It also discloses the prompt library is about prompt text, not MCPs. No contradiction with 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 long but well-structured, front-loading the purpose and core flow before diving into nuances like the KEY invoke behavior, install vs invoke, and the prompt library. It is a single dense paragraph with no bullets, which makes it harder to scan, but every sentence adds meaningful information and avoids 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 complex tool with 23 parameters, no output schema, and minimal annotations, the description is unusually complete. It covers all the main actions, the invite/install/invoke flow, auth/payment edge cases, permission requirements, and the prompt library subsystem. It misses explicit mention of the 'resume' action and some return-value shapes, but overall provides enough context for an agent to operate effectively.

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 with 0% description coverage, so the description must compensate. It does explain the critical action enum values and their roles (search, describe, install, invoke, list_tools, prompt actions), and it clarifies tool_id, arguments, and prompt variables implicitly. However, it does not detail parameters like limit, immediate, tier_slug, conversation, request_details, or resume, leaving significant gaps for full parameter-level understanding.

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 specific declaration: 'The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It then gives a concrete core flow (search → describe → invoke) and explicitly contrasts this tool's invoke behavior with permanent installation, distinguishing it from sibling tools like toolkits or connect. This is a specific verb+resource statement with clear scope.

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?

Usage guidance is explicit and actionable: 'Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile... so you pick the right tool_id → invoke RUNS that tool.' It also says 'Use install only to make an MCP PERMANENT... prefer invoke for a single/occasional use,' and 'list_tools lists what is callable right now.' These are clear when-to-use versus when-not-to-use instructions with named alternatives.

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 (readOnlyHint=true, idempotentHint=true, destructiveHint=false), the description adds critical behavioral details: it is hosted by the platform, requires no credentials, incurs a per-query cost with prepaid credit, and queries only public official data. It also clarifies legal responsibility (client as data controller). These are valuable context beyond annotations and do not contradict 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 appropriately sized at three sentences. It leads with the core purpose, then adds payment and legal context. Every sentence contributes useful information, though the legal disclaimer could be considered slightly tangential. Overall, it is well-structured and not verbose.

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 has a small surface area (two parameters, no output schema). The description covers the data type, access method, credentials, payment, and legal responsibility. It does not describe the exact return structure or the effect of 'completo', but for a simple query tool this is reasonably 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?

The schema has zero description coverage, so the description must compensate. It mentions 'DNI' as the input, which helps identify that parameter. However, the optional 'completo' boolean parameter is entirely unexplained, leaving the agent unclear about its semantics. This is a notable gap given 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 clearly states the tool's function: querying cadastral data of a person in Argentina using their DNI. It explicitly names the resource ('dados cadastrais de uma pessoa física na Argentina') and the input ('a partir do DNI'). It is also distinct from sibling tools (authenticate, connect, marketplace) which serve completely 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 provides clear usage context: it is hosted by the platform, requires no credentials, and is paid per query with prepaid credit. It also specifies that the data is of public access and the client is responsible for legitimate purpose (LGPD). However, it does not explicitly mention alternative tools or provide exclusions, but the purpose is so distinct that the context is sufficient.

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. The description adds context about including the conversation for reproduction, which is useful. Yet it doesn't disclose potential side effects, whether feedback is sent externally, or any rate limits, but the safety profile is partially covered by 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 no wasted words. The instruction about the conversation array is directly useful and not redundant with the schema.

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 relatively simple, but the description could be more complete: it lacks explanation of the 'message' and 'context' parameters, and there is no mention of what happens after submission. The conversation type mismatch adds a slight ambiguity. Despite this, it covers the core usage sufficiently for a low-complexity 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?

With schema description coverage at 0%, the description must compensate. It explains the 'conversation' parameter's purpose but leaves 'message' and 'context' undefined. Also, the description calls it a 'conversation array' while the schema declares it as a string, which is a subtle mismatch.

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 with a specific verb ('Report') and the resource (bug, missing feature, feedback). It distinguishes from unrelated sibling tools like 'authenticate' and '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?

It provides clear context on how to use the tool (include conversation array) and the general case for reporting bugs/feedback. However, it doesn't explicitly mention when not to use it or name alternative tools, but the siblings are unrelated, so this is acceptable.

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 safety profile is known. The description adds context about what is shown (platform and adapter versions) but does not disclose details like return format or whether any network call is involved. It adds minimal value 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?

The description is a single, concise sentence that delivers the core purpose immediately. There is no wasted wording or redundant 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 simple zero-parameter version query, the description states the main output clearly. However, since there is no output schema, explicitly mentioning the expected response shape would have been beneficial. Still, the tool is trivial enough that the description covers most of what an agent needs.

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 description is not required to explain parameter semantics. According to the rubric, a baseline of 4 applies for 0-parameter tools, and the description does not need to compensate for any schema 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 uses the specific verb 'show' and clearly identifies the resource ('current MCP platform and adapter versions'). This distinguishes it from sibling tools like 'authenticate', 'connect', and 'toolkit_info', which handle different concerns.

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 this tool (when version info is needed) but does not explicitly state alternatives or when-not-to-use. Since it is a simple informational tool, the usage context is reasonably clear, but there is no explicit 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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds valuable behavioral context beyond annotations by specifying the exact content returned (installed MCPs, connection status, accounts, catalog tool counts), which helps the agent understand what information is available.

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 purpose ('Returns the current toolkit state') and then lists the key details. Every word contributes meaning, 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's simplicity (no parameters), strong annotations, and the absence of an output schema, the description fully covers what the tool does and what it returns. It provides enough detail for an agent to decide when to call it and what to expect in the response.

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 vacuously documents 100% of parameters. The description correctly implies no inputs are needed, and the baseline for no-parameter tools is 4. No additional parameter guidance is required.

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 a specific verb ('Returns') and a clear resource ('current toolkit state'), enumerating exactly what is included: installed MCPs, connection status, connected accounts, and catalog tool counts. This is more specific than the generic tool name and clearly distinguishes it from siblings like 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 makes it clear when to use this tool: when you need the current toolkit state (installed MCPs, connection status, accounts, catalog counts). It doesn't explicitly name alternatives or exclusions, but the context is clear and no conflicting tool is described nearby.

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