Restituição IRPF

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

Discloses behavioral traits beyond annotations: it initiates a browser login, returns a link, and distinguishes between permanent (config) and session-only (token) usage. Annotations already state idempotency and non-destructiveness, so the description adds useful context without 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 somewhat verbose and rambling, mixing instructions and examples in a long sentence. However, it does lead with the purpose and all information is relevant, so it is acceptable but not tight.

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 the essential workflow and both usage modes. It lacks details about error handling or post-auth state, but is otherwise 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 and the only parameter (token) is optional. The description fully explains its meaning (a JWT access token) and the two calling conventions: with the token for session login, or without it to get the login link.

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: authenticating the user for MCP.AI in IDE agents. It specifies the workflow (browser login, copy token) and distinguishes it from sibling tools (none of which are authentication-related).

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 guidance: recommends adding the token to the server config for permanent access, or using the session-only login by pasting the token. Also explains when to call with no args to get the link vs. with the token.

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 destructiveHint. The description adds valuable conditional behavior: returns authenticated:true with empty pending[] when all connected, and returns connect_url and per-install URLs when credentials are missing. This goes 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?

The description is two sentences, front-loaded with the main purpose, and every sentence adds useful information without 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?

For a simple zero-parameter tool with no output schema, the description covers the key states (all connected vs. missing credentials) and is sufficient for an agent to know what to expect. It doesn't need to explain return values since no output schema exists, but it does.

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 is empty and coverage is 100%. Per the rubric, the baseline for 0 params is 4; no parameter information 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 clearly states the tool returns connection status and URLs, with specific behavior for different states. This distinguishes it from sibling tools like 'authenticate', which likely establishes connections rather than checking status.

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 the tool is used to check connection status and obtain URLs when needed, but it does not explicitly mention when not to use it or name alternatives like 'authenticate'. Context is clear, but exclusions are missing.

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, it discloses important behaviors: invoke runs tools one-off without installing, returns connect links for credentials and checkout links for payment, writes require workspace owner/admin, and prompt library links open without login. This is rich behavioral context that helps the agent predict 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 and information-rich, front-loading the core flow and then covering edge cases and the prompt library. It contains no wasted words, but the long single-paragraph structure could be improved with bullet points for scanability. Overall, it earns its length.

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 (23 params, no output schema, many actions), the description covers nearly all necessary context: core workflow, auth requirements, payment edge cases, permission levels, and the separate prompt library. It leaves minimal gaps for an agent to safely select and invoke the right 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 0% schema description coverage, the description carries the burden. It explains the semantics of the 'action' parameter thoroughly and implies the roles of other parameters (query, limit, mcp_id, tool_id, arguments, prompt_* fields) through the workflow. However, many of the 23 parameters are not individually documented, so the agent must rely on names and defaults for some fields.

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 marketplace is 'the official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It enumerates the core actions (search, describe, invoke, install, etc.) and distinguishes itself from siblings by describing its own role as a catalog and runtime.

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: 'prefer invoke for a single/occasional use' and 'Use install only to make an MCP PERMANENT'. It outlines a core flow (search→describe→invoke) and explains when to use list_tools, subscribe/cancel, report_bug, request_mcp, and the prompt library actions, plus auth/payment retry steps.

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=false, destructiveHint=false, and idempotentHint=true, covering the safety profile. The description adds guidance to include the conversation array for reproduction, but it does not disclose return values or request/response behavior, which is a 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?

The description consists of two short, purposeful sentences with no filler. It front-loads the purpose and immediately provides a key usage instruction.

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 does not mention expected return values or confirmation behavior. It also leaves the 'context' parameter undocumented. However, for a simple tool with annotations covering safety, it covers the main purpose and one parameter reasonably well.

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 must compensate. It adds meaning by clarifying that 'conversation' is an array of recent messages and implies 'message' is the bug/feedback text, but the 'context' parameter remains entirely unexplained.

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 'Report a bug, missing feature, or send feedback' with a specific verb and resource, clearly distinguishing this from sibling tools like authenticate, connect, marketplace, restituicao_irpf_consultar, show_version, and toolkit_info.

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 (reporting bugs, missing features, or feedback), and the instruction to include the conversation array adds useful usage direction. However, it does not explicitly mention when not to use or name 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context beyond annotations: hosted by the platform, no credentials, pay-per-query pricing, and the public/LGPD data classification. This enriches the behavioral understanding 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 three sentences and front-loads the core purpose. The second sentence explains the access/payment model, and the third adds LGPD context. All sentences earn their place; it is concise without being over-stuffed.

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 straightforward lookup tool with no output schema, the description covers the essential aspects: exact purpose, required parameters, authentication requirement (none), cost model, and data sensitivity. It doesn't describe response format or error conditions, but those are not explicitly needed for this simple read operation.

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, so the description must carry the meaning. It does name the three required parameters (CPF, birth date, tax year) and ties them to the query purpose, but it omits the optional 'completo' parameter and does not explain formats, patterns, or value constraints. This is a partial but not complete compensation for the schema gap.

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 it queries the income tax refund status for an individual using CPF, birth date, and tax year. This specific verb (consulta), resource (situação da restituição do IRPF), and parameter set distinguish it sharply from sibling tools (authenticate, 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?

It provides clear context: the tool is hosted on the platform, requires no credentials, is paid per query with prepaid credit, and accesses public data. While it doesn't explicitly name alternative tools or give 'when not to use' exclusions, the context is sufficient to understand when and how to invoke it.

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, which fully cover the safety profile. The description adds no additional behavioral context beyond confirming a read operation. It is consistent with annotations, so no extra credit for enrichment.

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 directly states the tool's purpose. There is no wasted wording or irrelevant detail.

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, parameterless, read-only tool with complete annotations and no output schema, the description fully conveys what the tool does. No additional context is needed; it is a complete and sufficient tool definition.

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, so the baseline is 4. There is nothing for the description to add regarding parameters, and schema coverage is trivially complete.

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 with a specific verb ('Show') and resource ('current MCP platform and adapter versions'). It distinguishes from siblings like toolkit_info by focusing on version information, making it 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 implies usage (when you need version info) but does not explicitly state when to use it versus alternatives like toolkit_info. There are no exclusions or conditional guidance, so it only meets the 'implied usage' level.

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, establishing safety. The description adds valuable context about exactly what information is returned, going beyond the minimal annotation coverage without contradicting it.

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 primary purpose and then enumerates the return contents. Every word earns its place with no redundancy.

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 zero parameters and no output schema, the description fully covers what the tool does and what it returns. It is complete for an agent to select and invoke the tool without ambiguity.

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 baseline is 4. The description correctly focuses on output rather than input, and there is no need to compensate for parameter documentation.

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 the current toolkit state, listing specific components (installed MCPs, connection status, accounts, catalog tool counts). This is a specific verb+resource description that differentiates it from sibling tools like show_version or marketplace.

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 this tool versus alternatives, nor does it mention exclusions or alternatives. Usage is implied rather than directly guided.

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