Prefeitura RS Caxias do Sul: Certidão Negativa de Débitos

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

The description adds meaningful behavioral context beyond annotations: it distinguishes session-only login from permanent config-based auth, explains that no-args returns a link, and clarifies the token is a JWT. It does not contradict the annotations, though it could say more about what the tool returns or changes in auth state.

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 tool's purpose, and packed with actionable details. Every clause contributes useful information without redundancy or 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 single-parameter authentication tool with no output schema, the description covers the main usage modes, token acquisition, and the permanent-config alternative. It is slightly incomplete regarding return values or error scenarios, but it provides enough context for an agent to invoke the tool 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 only lists 'token' as an optional string with no description, so the description carries the full burden. It explains that the token is a JWT copied from the browser and that passing it enables session-only login, while omitting it returns the login link. This sufficiently 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 as an authentication flow for MCP.AI in IDE agents, explaining how to log in, copy the token, and use it. It distinguishes the tool's function from generic operations by giving concrete actions: get a link, paste a token, or configure a permanent header.

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 strong usage guidance: it explains when to call with no args to get a link and when to call with a pasted token. It also recommends the config-header approach as the best permanent option, but it does not explicitly contrast this tool with sibling tools like 'connect'.

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 readOnly and idempotent behavior, so the description adds value by explaining conditional return shapes (authenticated:true vs. connect_url). It provides meaningful behavioral context beyond the safety hints 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?

Two sentences, each carrying distinct and necessary information. The description is front-loaded with the core purpose and then details conditional behavior with 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?

The tool is simple (no parameters, no output schema) but the description adequately covers its primary return values and key conditional states. It does not detail the exact structure of 'pending[]' or 'connect_url', but this is sufficient for a status check 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 and schema coverage is 100% (empty schema). Per the rubric, a no-parameter tool with full schema coverage receives a baseline of 4 since there are no parameter semantics 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 clearly identifies the tool as returning connection status and URLs, which is a specific verb+resource pairing. It implicitly distinguishes from sibling tools like authenticate by focusing on status reporting rather than performing 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 implies use cases by describing conditional outcomes (all providers connected vs. credentials missing), but does not explicitly state when to prefer this tool over alternatives or mention exclusions. The usage context is implied, not 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?

The description goes well beyond the annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false). It discloses critical behavioral traits: invoke runs even when MCP is not installed (one-off), returns connect/checkout links when auth/payment is needed, requires workspace owner/admin for writes, and distinguishes installed_in_toolkit vs installed_in_workspace. It also explains subscription management and prompt publishing. No contradiction with annotations. The description carries the full burden of these side effects and safety characteristics.

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 about 250 words, which is substantial but justified given the tool's complexity (14 actions, 23 parameters). It is well-structured: starts with overall purpose, then the core flow (search → describe → invoke), then key differentiators (install vs invoke, permissions), then enumerates remaining actions, and finally covers the prompt library. The most important information is front-loaded. Not a single wasted sentence, though it could be slightly tightened.

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 with 23 parameters, no output schema, and 14 actions, the description is remarkably complete. It explains the discovery flow, one-off execution, permanent installation, credentials/billing handling, permission requirements, list_tools, subscription management, bug reporting, request_mcp, and the entire prompt library subsystem with its actions. It even covers how search/describe flag installation status. It addresses all major usage scenarios an agent would encounter. The only minor gap is parameter-level detail, but the description compensates through action-specific 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?

Schema coverage is 0%, so the description must compensate, but it only loosely references parameters like action, mcp_id, tool_id, arguments, and prompt_* fields. It explains the meaning of key actions and their typical parameters implicitly, but it does not systematically map each of the 23 parameters to their roles. For example, 'prompt_vars', 'prompt_targets', 'conversation', 'report_context', and 'cancel_reason' are not explained individually. The description gives high-level semantics but leaves many parameter details to be inferred from action context.

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 starts with a clear, specific statement: 'The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It immediately distinguishes from sibling tools (authenticate, connect, etc.) by framing itself as the central hub for discovery and execution. The scope is precise, listing distinct capabilities like searching, invoking, installing, and prompt library access, so the agent knows exactly what this tool covers and what it does not.

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 guidance on when to use which action: 'prefer invoke for a single/occasional use' vs 'use install only to make an MCP PERMANENT'; 'list_tools lists what is callable right now'; and it explicitly contrasts invoke with install, handling credentials (connect link) and billing (checkout/top-up link). It also distinguishes the prompt library as separate from MCPs. This gives the agent clear decision criteria for selecting the right action within the tool and relative to siblings.

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 mark it read-only, idempotent, and non-destructive. The description adds significant behavioral context beyond that: it incurs a cost (pay-per-query), it requires no platform credentials, it queries official Brazilian sources, and it clarifies LGPD data-responsibility terms. This is exactly the kind of extra information an agent needs to invoke the tool safely and appropriately.

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 in Portuguese, each adding distinct information: purpose, payment/credentials, and data governance. It is concise with no filler, though it could be more explicitly structured. Overall it earns its place without being 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 no output schema, so the description should ideally clarify what the caller receives. It explains the source and legal context but does not mention the response format or content (e.g., whether it returns the certificate itself). It also omits any mention of how CPF/CNPJ are used. For a simple query tool, the description leaves the output behavior ambiguous, making it only minimally 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 input schema defines only 'cpf' and 'cnpj' as strings with no descriptions, and the schema coverage is 0%. The description does not elaborate on these parameters—it never mentions that CPF/CNPJ are the required identifiers, nor does it say when to provide which. Since the description fails to compensate for the lack of schema documentation, parameter semantics are weak.

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 queries a Certidão Negativa de Débitos (CND) from Prefeitura RS Caxias do Sul via an official source. It uses specific language ('consulta em fonte oficial') and the tool name echoes the action, making the purpose unmistakable. Sibling tools are unrelated platform utilities, so this stands out as a dedicated consultation tool.

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 platform credentials, and is paid per query via prepaid credit. It also notes that the data is the same available to citizens and is not confidential. However, it does not explicitly mention when to use this tool versus alternatives or when not to use it, so it stops short of full exclusion 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 indicate this is not read-only, not destructive, and is idempotent. The description adds that conversation context should be included for reproduction, but it does not disclose what happens after reporting, whether a confirmation is returned, or any side effects. This is adequate 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?

Two sentences, front-loaded with purpose, and no filler. Every sentence contributes actionable 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?

The tool is relatively simple, and the description covers purpose and one key parameter. However, with no output schema and no explanation of return behavior/side effects, the agent has incomplete expectations about what invoking this tool will produce.

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 explains 'conversation' as containing recent messages for reproduction, but does not clarify 'context' or explicitly tie 'message' to the bug report content. Two of the three parameters remain semantically under-specified.

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?

Description clearly identifies the action ('Report') and the resource ('a bug, missing feature, or send feedback'). This distinguishes the tool from unrelated siblings like authenticate, connect, 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?

The description states when to use the tool by naming three use cases: bug reports, missing feature requests, and general feedback. It also advises including the conversation array for reproduction. No exclusions or alternative tools are mentioned, so it stops short of a 5.

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 only the specific scope ('platform and adapter versions') but does not describe output format or dynamic behavior.

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?

A single, front-loaded sentence that fully states the tool's purpose with no wasted words. It is appropriately minimal for a zero-parameter read-only tool.

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 version-information tool with no parameters and strong annotations, the description is nearly complete. It could mention the output format, but the core purpose is sufficiently clear for an agent to invoke it 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 tool has zero parameters, so parameter semantics are not a burden. The description needs no parameter details, and the baseline of 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 uses the specific verb 'Show' with a clear resource: 'the current MCP platform and adapter versions.' This distinguishes it from sibling tools like authenticate and report_bug, while remaining clearly scoped to 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?

No guidance is given on when to use this tool versus alternatives such as toolkit_info. The simple read-only nature implies usage, but no explicit context or exclusions 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, covering safety. The description adds valuable content details (what state is returned) beyond the annotations. It doesn't mention potential performance costs or snapshots, but for a read-only status tool this is adequate.

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?

A single, well-structured sentence that front-loads the purpose and lists the returned data elements. Every word contributes value, with no filler or 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?

For a zero-parameter, read-only tool with no output schema and clear annotations, the description fully explains the return contents (MCPs, status, accounts, tool counts). Nothing critical is missing, and the tool is simple enough that this is 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 tool has zero parameters, so the baseline is 4 per the rubric. The description correctly omits parameter details since there are none, and no additional meaning 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 the current toolkit state, specifying exactly what information is provided: installed MCPs, connection status, connected accounts, and catalog tool counts. This is specific and distinguishes it from siblings like show_version which likely returns version info only.

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?

While it doesn't explicitly say when to use this tool vs alternatives, the purpose is self-evident as a status/overview tool. It gives clear context (returns overall state) but no exclusions or alternative names, so it falls slightly short of a 5.

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