SEFAZ SC DEC: Caixa Postal

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

Beyond annotations, the description explains behavior: returning a login link when called with no args, accepting a JWT for session login, and the difference between permanent and session-only connections. It doesn't contradict the idempotentHint or other 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?

Three sentences, each packed with necessary information; no fluff. Structured clearly with 'Best' and 'Or'.

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 simple schema and no output schema, the description covers the core workflow and edge cases (permanent vs session, no-arg vs token). It's sufficient 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 provides no description for 'token' (0% coverage). The description fully compensates by specifying the token is a JWT to paste for session login, and that omitting it retrieves the 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 purpose: authenticating MCP.AI for IDE agents via browser login and access token. It distinguishes from siblings like 'connect' by detailing the specific auth flow and token handling.

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?

Explicitly provides two usage paths: permanent config via header vs session-only token paste, with guidance on when to choose each ('Best: add it to config' for permanent). It also explains how to invoke with or without 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 (readOnlyHint=true, idempotentHint=true, destructiveHint=false) already signal safety. The description adds behavioral details about states (when all connected vs missing credentials) and the structure of returned data (authenticated, pending[], URLs), going beyond annotations 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?

Two sentences with zero filler. Front-loads the core purpose and then specifies behavioral details. Every sentence contributes meaning.

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 parameterless read-only status tool with no output schema, the description fully explains what to expect in both states. It is complete and self-contained.

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?

No parameters exist, and schema description coverage is 100% (vacuously). Per rubric, zero params sets a baseline of 4. The description adds no parameter info because none are 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 scenarios (all connected vs. missing credentials) that distinguish it from siblings like authenticate. It uses a specific verb ('returns') and identifies the resource.

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 (to check connection status) but does not explicitly compare to alternatives like authenticate or state when not to use it. It provides context for expected outcomes but no exclusion criteria or explicit alternative 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?

The description discloses important behavioral traits not visible in annotations: invoke runs a tool even when the MCP is not installed, one-off installs happen behind the scenes, credentials/billing failures return connect/checkout links, and writes require workspace owner/admin. It also clarifies the side effects of install vs invoke. This exceeds the annotations' basic readOnlyHint=false and provides substantial operational context.

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 dense and every sentence contributes operational knowledge. It is front-loaded with the core purpose and critical distinction of invoke-vs-install. It could benefit from bullet structure or separated sections to avoid the single dense paragraph, but it is not padded or redundant.

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 broad multi-action surface, 23 parameters, and no output schema, the description does remarkably well at covering major capabilities, billing side effects, authentication, writes, and the prompt library. However, it leaves gaps such as the 'resume' action, complete return-shape descriptions, and explicit per-action parameter usage.

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%, and the description does not compensate for all 23 parameters with explicit per-parameter guidance. It explains high-level action names and major flows, e.g., action=search, describe, invoke, and publish_prompt/slug, but does not systematically map input parameters to each action. Some parameters remain underspecified, like resume, conversation, and arguments formatting.

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 exact role of the tool: the official mcp.ai marketplace, acting as both a catalog and runtime for MCPs. It lists specific sub-capabilities (search, describe, invoke, install, subscribe, prompt library) and clearly distinguishes the actions from one another. This goes well beyond the generic tool name '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?

It gives explicit when-to-use vs alternatives: 'prefer invoke for a single/occasional use', 'Use install only to make an MCP PERMANENT', and 'list_tools lists what is callable right now'. It also explains the core flow search → describe → invoke, plus the fallback of request_mcp when nothing fits. This is model guidance with actionable direction.

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 a usage requirement (include conversation for reproduction) but does not disclose any side effects, persistence, or response behavior. It contributes some context but not rich behavioral detail.

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 purpose, and every word adds value. No fluff or redundancy, making it highly efficient.

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 description lacks essential details: what to put in 'context', how to format the conversation string, expected message content, and success/failure behavior. With no output schema and minimal annotations, this incomplete guidance could cause incorrect invocation.

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 mentions the 'conversation array' but fails to explain the 'context' or 'message' parameters, and it calls the conversation a 'array' while the schema defines it as a string (likely JSON-encoded). This partial explanation leaves two of three parameters unelaborated.

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 verb ('Report') and resource ('bug, missing feature, or send feedback'). It is distinct from siblings like authenticate or marketplace, and the purpose 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 description provides clear context on when to use the tool (to report bugs/feedback) but does not explicitly mention when not to use it or compare with alternatives. The instruction to include the conversation array gives a specific usage hint, but there is no exclusionary 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 the operation read-only, idempotent, and non-destructive, and the description adds useful context beyond that: prepaid per-query cost, absence of platform credentials, publicly available data, and LGPD controller responsibility. This meaningfully supplements the behavioral annotations without contradicting 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 compact and front-loaded with the key purpose, followed by cost and compliance context. Each sentence contributes something, though some compliance language could be trimmed without harming clarity.

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 six parameters and the absence of an output schema, the description is not complete enough for a new agent to confidently invoke the tool. It conveys the general domain and payment model, but not the meaning of inputs or the expected result format.

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%, and the description comments on none of the six parameters. Required fields like pkcs12_cert and pkcs12_pass, and optional fields like login_usuario, login_senha, cnpj, and ignora_nao_lidas, are left completely 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 'SEFAZ SC DEC: Caixa Postal, consulta em fonte oficial', clearly identifying the operation as a query against an official SEFAZ source. It states a specific resource and verb, and is distinguishable from the unrelated sibling tools, though it does not describe exactly what data is returned.

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?

No explicit when-to-use or when-not-to-use guidance is given, and no alternative tool is named. The description offers general context about prepaid credit and official-source data, but the agent is left to infer the appropriate invocation 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 the tool as read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds that it reports both MCP platform and adapter versions, but discloses no additional behavioral traits such as whether it queries live services or returns cached values.

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 fully states the tool's purpose. There is no filler, repetition, or unnecessary 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 zero-parameter, read-only version-query tool, the description is complete. An output schema is not needed here, and the annotations sufficiently cover safety and idempotency 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 tool has zero parameters, so parameter documentation is unnecessary. Schema description coverage is effectively 100%, and the baseline of 4 is appropriate because there are no parameter semantics the description needs to clarify.

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 a clear resource ('current MCP platform and adapter versions'), making the tool's function immediately obvious. It distinguishes itself from siblings like toolkit_info by focusing specifically on version information.

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 the tool—whenever version information is needed—but it does not explicitly state alternatives or exclusions. It provides a clear purpose but no guidance on when not to use it or which sibling tools might be relevant.

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, so the description doesn't need to repeat safety. It adds detail about the content of the state, which is useful. However, it does not mention any potential caveats like authentication requirements or that some connections might be slow, so it's reasonable but not rich.

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 is packed with specific details (installed MCPs, connection status, accounts, catalog tool counts). It is concise, front-loaded with the purpose, and every part 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?

Given the tool has no parameters and no output schema, the description does a good job outlining what the response will contain. It is complete enough for a read-only info tool, though it could mention if the response is a summary or detailed report, but that is minor.

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 0 parameters, so the description is the sole source of semantics. It explains what the return value will contain (state of toolkit), which is effectively parameter semantics. Baseline for 0 params is 4, and the description satisfies that by explaining the purpose of the call.

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 toolkit state with specific elements (installed MCPs, connection status, accounts, catalog tool counts). It is clear and distinguishes from siblings like authenticate or show_version, though it doesn't explicitly name them.

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 you need an overview of the toolkit state), but it does not explicitly contrast with siblings or mention when not to use it. It is adequate but lacks explicit alternatives.

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