SEFAZ RO DEC: Caixa Postal

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

Annotations indicate idempotentHint=true and readOnlyHint=false. The description adds behavioral context: it explains that providing a token establishes a connection, and that no args returns a login link. It does not contradict annotations. However, it could further disclose that the tool does not perform any destructive actions, but the annotations already cover that.

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 paragraph of moderate length, but it is dense with actionable instructions. It is somewhat long but every sentence provides value, explaining both permanent and session-only login methods. It could be slightly more structured (e.g., bullet points), but it is not bloated.

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 (one optional parameter, no output schema), the description covers the main usage patterns well. It includes the auth flow, both login methods, and the meaning of arguments. However, it could benefit from stating what happens after successful authentication (e.g., what the agent can do next), but this is a minor gap.

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 one optional parameter 'token' with no description. The description explains that 'token' should be a JWT pasted by the user for session-only login, and that omitting it returns a link. This adds significant meaning beyond the schema, despite the schema having 0% coverage. The semantics are clear and well-addressed.

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 authenticates the user by logging in and obtaining a token. It distinguishes itself from siblings like 'connect' and 'marketplace' by focusing on authentication for MCP.AI IDE agents. However, the purpose is somewhat embedded in a broader set of instructions, which slightly reduces clarity.

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 explicit usage scenarios: logging in, copying token, adding it to config for permanent access, or pasting it for session-only. It also explains when to call with no args to get the link. This clearly guides the agent on when and how to use the tool, distinguishing it from 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, idempotentHint, and non-destructive behavior. The description adds value by explaining that the return payload varies depending on connection state, which is behavioral context beyond the annotations. It doesn't contradict anything.

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 purpose, then behavioral nuance. No wasted words, efficient and clear.

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 must explain return values. It covers the two key scenarios (connected vs missing credentials) but doesn't enumerate all possible fields (e.g., what 'pending' contains, or exact URL format). Still, it's sufficient for a status-checking tool with sibling 'authenticate' nearby.

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 avoids inventing parameter details and focuses on behavior. It doesn't need to compensate for missing schema info.

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 what the tool does: 'Returns connection status and URLs.' It specifies the resource (connection status) and provides distinguishing details (authenticated, pending, connect_url). It is distinct from siblings like 'authenticate' since it's about status, not authentication.

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 explains behavior under two conditions (all connected vs missing credentials) which implies when to use it (to check status). However, it does not explicitly state when not to use it or mention alternatives among siblings. Context about using 'authenticate' for actual authentication is absent.

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 are minimal (readOnlyHint=false, openWorldHint=true), so the description carries the full burden — and it delivers. It discloses non-obvious behaviors: invoke executes one-off runs without installing or 'bloating the tool list,' returns a connect link for credential needs, returns a checkout/top-up link for empty wallets, and flags that writes require workspace owner/admin. These side-effect and prerequisite disclosures go well 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 content is front-loaded and every sentence earns its place — there is no filler. However, the entire description is one dense run-on paragraph exceeding 400 words, with no section breaks, bullets, or per-action headers despite covering 14 actions and 23 params. The information density is high but the lack of structure makes it hard for an agent to scan quickly.

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 extreme complexity (23 params, 14 actions, no output schema, no required params), the description covers the core flow, the invoke/install distinction, auth/payment behaviors, permission requirements, and the prompt library comprehensively. Residual gaps: per-action return shapes (what search actually returns beyond installed flags, what invoke's success response looks like) are not specified, forcing the agent to infer response contracts. Still, it is unusually complete for a tool this sprawling.

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 coverage over 23 parameters, the description must compensate. It explains the central action enum exhaustively, covers mcp_id/tool_id/arguments through the flow narrative, describes the prompt-library params (prompt_vars as {{variables}}, shareable slug links), and cancel params via billing context. However, several params remain undocumented in both schema and description: immediate, tier_slug, conversation, limit, report_context, and request_details. The description does heavy lifting but leaves roughly a third of the params 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 opens with a specific, unambiguous statement — 'The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them' — and enumerates the 14 distinct actions it dispatches. It clearly differentiates from siblings: report_bug, list_tools, and request_mcp are positioned as separate facets, and the marketplace's role as catalog+runner is distinct from 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?

Provides explicit when-to-use guidance: 'prefer invoke for a single/occasional use' versus 'Use install only to make an MCP PERMANENT in the active toolkit,' plus 'request_mcp asks us to build a NEW MCP when nothing fits.' The core flow is laid out step-by-step (search → describe → invoke), and billing actions (subscribe/cancel) and prompt-library actions are given clear contexts. This is textbook usage scoping 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?

Annotations already declare readOnlyHint=false, idempotentHint=true, and destructiveHint=false, covering basic safety. The description adds behavioral context by instructing to include the conversation array for reproduction, indicating the tool collects diagnostic info. It does not contradict annotations and provides additional value.

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, focused sentence that front-loads the purpose and the key instruction. It contains no fluff or redundant information, making it extremely concise and well-structured.

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 3 parameters and no output schema, the description covers the essentials: purpose and the key nuance of including the conversation. The 'context' parameter remains vague, but given the tool's simplicity and the availability of annotations, the description is largely 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?

With schema description coverage at 0%, the description must compensate. It explains the 'conversation' parameter ('Include the conversation array with recent messages for reproduction') but leaves 'context' and 'message' without explicit explanation, though 'message' is self-evident as the report text. This partial compensation warrants a mid-level score.

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') with a specific verb and resource, and distinguishes it from sibling tools (authenticate, connect, etc.) by focusing on issue reporting. It also adds a key detail about including the conversation array for reproduction, which clarifies 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?

The description implies when to use it (when encountering a bug, missing feature, or needing to send feedback) via its clear purpose, but does not explicitly mention alternatives or exclusions. It provides clear context for usage without formal 'when-not' guidance, which aligns with a score of 4.

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 beyond the annotations by explaining that the data is the same as available to citizens, clarifying it is not confidential, and detailing the LGPD compliance (controller role). It also discloses hosting details and the prepaid payment model, which are crucial behavioral traits not present in 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, information-dense paragraph with no fluff. Every sentence adds value: purpose, official source, data classification, payment, hosting, and LGPD note. It's concise yet thorough.

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 4-parameter tool with no output schema, the description covers all key contexts: what it does, legal basis, payment, data responsibility, and security aspects. It's appropriately complete for the tool's complexity, providing essential context without requiring further explanation of return values.

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, but the description adds meaning by explaining the context of credentials ('sem credenciais da plataforma') and the payment model, which helps understand pkcs12_cert/pass. However, it does not explain the meaning of 'cnpj' or 'ignora_nao_lidas', which are critical parameters, so it doesn't fully compensate 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 indicates this is a consultation tool for SEFAZ RO 'Caixa Postal' using official sources, which is more specific than just repeating the tool name. It distinguishes itself by adding details about official data, prepaid credits, and LGPD compliance. However, it could be crisper in naming the exact action (e.g., 'query mailbox messages') and how that differs from siblings.

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 it (when querying official Brazilian data) and gives context about payment and LGPD, but it does not explicitly state when NOT to use it or name alternative tools. There's no direct guidance on choosing this over siblings, so the usage guidance is mostly implied rather than explicit.

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 communicate readOnlyHint, idempotentHint, and non-destructive behavior. The description adds value by specifying that it reports both platform and adapter versions, which is useful context, but it does not disclose additional behavioral details such as output format.

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, clear sentence with no redundant words. It front-loads the core purpose and earns its place without padding.

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 zero-parameter, read-only, idempotent version-inspection tool, the description is complete. It clearly states what the tool returns, and the sibling tools do not suggest any missing context or hidden requirements.

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 baseline is 4. No parameter-level explanation is needed, and the description fully aligns with the empty input 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 a specific verb ('Show') and clearly identifies the resource: current MCP platform and adapter versions. It is distinct from sibling tools like authenticate or connect, and the output scope is immediately understandable.

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 intended use is implied—call this when you need version information—but there is no explicit guidance about when to use this versus alternatives such as toolkit_info or other query-like tools. No exclusions or alternative suggestions are provided.

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, clearly establishing a safe read-only operation. The description goes beyond annotations by detailing the specific state categories returned, giving the agent an accurate picture of the output contents. No contradictory behavioral notes are present.

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, front-loaded sentence that communicates what, why, and the content scope in a concise list form. Every phrase earns its place and there is no fluff or redundant metadata.

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 zero-parameter informational read, the description is complete. It lists the exact categories of state that will be returned, which is sufficient given the read-only annotations and the absence of complex input/output schemas or nested objects. The agent has enough context to call the tool and interpret the results with high confidence.

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 takes zero parameters, so no parameter-specific documentation is needed. The schema coverage is effectively complete, and the description focuses appropriately on return value composition rather than trying to describe nonexistent parameters.

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 what the tool returns: the current toolkit state, including installed MCPs, connection status, connected accounts, and catalog tool counts. It uses specific, concrete language that differentiates it from siblings like show_version (version info) and connect (establishing 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 implies a diagnostic/status-reporting use case, making it seems obvious that an agent would call this to inspect the environment, but it does not explicitly state when to use this tool over alternatives such as show_version or marketplace. There is no direct mention of when not to use it or which sibling is the better choice for related needs.

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