Prefeitura MG Araújos: Certidão Negativa de Débitos

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

Annotations already convey idempotency and non-destructiveness, and the description adds meaningful context: the distinction between permanent config (non-expiring) and session-only login, the JWT format, and the behavior of the no-args call. This goes beyond the structured data 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 front-loaded and packed with necessary details, but the run-on sentences mix context, best practice, and usage instructions. It could be split into clearer steps, but remains appropriately sized.

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 one optional parameter and no output schema, the description covers the main invocation patterns and the distinction between permanent and temporary authentication. It doesn't detail error handling or the exact response when a token is passed, but this is adequate for agent 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?

The schema has a single optional `token` property with no description (0% coverage), but the description explains that it is a JWT for session-only login and that omitting it returns a link. This adds crucial meaning beyond the bare 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 clearly identifies the tool as handling authentication for MCP.AI IDE agents, describing the browser login and token flow. It distinguishes itself from siblings like connect and marketplace by focusing specifically on the authentication token acquisition and session vs. permanent configuration.

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 guidance: call with no args to get a link, or pass a token for session-only login, and recommends configuring a permanent header. It does not explicitly compare against sibling tools, but the authentication context makes the tool's role 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?

Annotations already declare the tool as safe (readOnlyHint, idempotentHint, destructiveHint=false). The description adds valuable context about actual response behavior: what is returned when all providers are connected (authenticated:true and empty pending[]) versus when credentials are missing (connect_url and per-install URLs). This goes beyond the annotations to explain the outcomes.

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 primary purpose. The second sentence adds essential behavioral details in a compact form. No redundant or filler words are present.

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 a simple status check with no parameters and robust annotations. The description covers the two most relevant states (all connected and missing credentials). However, it does not mention the case of partial connectivity or any other edge states, which could be relevant. Given the tool's simplicity, the description is largely sufficient but not exhaustive.

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 input schema is empty. The description correctly avoids mentioning parameters, and the baseline for zero parameters is 4 since there is nothing to clarify. The description adds no param semantics because none exist, but this is appropriate.

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 verb and resource: 'Returns connection status and URLs.' It clearly distinguishes itself from siblings like authenticate (which likely initiates the connection flow) by focusing on status retrieval. The conditional behavior further clarifies its purpose.

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 this tool is used to check connection status, and the sibling list suggests authenticate is the complementary action. However, it does not explicitly state a preferred usage scenario or contrast with alternatives. The guidance is implicit 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?

Adds substantial behavior beyond annotations: one-off execution semantics ('invoke works even when the MCP is NOT installed... without bloating the tool list'), credential/payment failure paths (returns connect link or checkout/top-up link, user opens then retry), installation side effects, and permission requirements ('Writes... require workspace owner/admin'). The destructiveHint=false annotation is consistent with framing uninstall/cancel as permission-scoped writes rather than data destruction. Rich context that materially changes how an agent should act.

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 dense and every sentence carries real information, but the formatting is a single ~400-word wall of text with no bullets, headers, or per-action breakdown. For a dispatcher with 14 actions, structuring by action flow (marketplace flow, then prompt library, then permissions) would aid scanning. Length is justified by complexity, but the lack of visual structure hurts an agent trying to quickly locate a specific action's semantics.

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?

Strong coverage for a very complex tool: core flow, all 14 action values, permission model, edge cases (connect link, empty wallet checkout), install-vs-invoke consequences, prompt library behavior. With no output schema, the description partially addresses returns (describe returns full profile with pricing/auth; publish_prompt returns shareable mcp.ai/p/<slug>) but omits return formats for search, invoke results, and list_tools. Notable but not crippling gaps given the tool's breadth.

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 and 23 parameters, the description carries heavy burden. It explains the action enum values well and implies the main flow parameters (query, mcp_id, tool_id, arguments via the search/describe/invoke narrative, prompt_slug/prompt_body/vars via the prompt library). But several parameters go unexplained: immediate, tier_slug, conversation, message, prompt_tool, report_context, request_details, prompt_category, cancel_reason, cancel_comment. The description compensates only partially for the schema coverage 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 this is the mcp.ai marketplace catalog and execution engine, with an explicit core flow (search → describe → invoke). It distinguishes the tool from siblings (authenticate, report_bug, toolkit_info) by positioning it as the discovery/execution dispatch tool. However, the dual-domain scope (MCP marketplace + prompt library) packed into one tool dilutes clarity — the 'official catalog' framing doesn't prepare the reader for the substantial prompt-library sub-functionality.

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', 'Use install only to make an MCP PERMANENT', and 'request_mcp asks us to build a NEW MCP when nothing fits'. The core flow is spelled out (search discovers → describe returns profile → invoke runs). It lacks a few edge-case usage notes (when to use search vs list_tools explicitly, when subscribe vs cancel applies), but the install-vs-invoke contrast is genuinely actionable.

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 readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds valuable behavioral context: the tool is hosted, requires no credentials, is paid per query, and accesses only non-confidential public data. It also discloses LGPD data controller responsibilities, which 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 concise and front-loaded with the primary purpose in the first sentence. Subsequent sentences add necessary operational and legal context (payment, credentials, data source, LGPD) without being overly verbose. Each sentence contributes distinct 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 read-only query tool with two string parameters and no output schema, the description is largely complete. It covers critical operational aspects (payment, credentials), data nature (official, non-confidential), and legal context (LGPD). The only notable omission is parameter documentation, which was already penalized in parameter semantics.

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 includes cpf and cnpj as string parameters with no descriptions, and schema description coverage is 0%. The description does not explain these parameters, their expected formats, or when to use one over the other. This is a significant gap, as the agent cannot infer parameter semantics from the tool description alone.

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: consulting Certidão Negativa de Débitos (negative debt certificate) from Prefeitura MG Araújos through an official source. The verb 'consulta' combined with the specific resource distinguishes it from the unrelated sibling tools like authenticate 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 provides clear context for use: it is hosted on the platform, requires no platform credentials, and is paid per query with prepaid credit. It does not explicitly mention alternatives, but the sibling tools are generic platform utilities, so no direct alternative exists. The guidance is sufficient for an agent to decide when this tool is appropriate.

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 convey the core safety profile (readOnlyHint=false, idempotentHint=true, destructiveHint=false). The description adds useful context about including the conversation for reproduction, but otherwise does not disclose side effects, return behavior, or authentication needs. This is acceptable 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, front-loaded sentence that conveys purpose and the key usage instruction in about 16 words. Every word earns its place; no filler or repetition.

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 no output schema and three parameters, the description is reasonably complete. It explains the core purpose, names the relevant parameter, and gives the reproduction guidance. Minor gaps remain around the meaning of 'message' and 'context', but the schema identifies them.

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 adds meaning for 'conversation' (recent messages for reproduction), but does not clarify the required 'message' parameter or the optional 'context' parameter. This is only partial compensation for a 3-parameter tool.

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 action ('Report') and the resource ('a bug, missing feature, or send feedback'), making the tool's purpose immediately obvious. It also distinguishes itself from the unrelated sibling tools by describing a user-facing reporting function.

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 for when to use the tool: whenever the user needs to report a bug, missing feature, or provide feedback. It does not explicitly list exclusions or alternatives, but the usage context is unmistakable and direct.

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, and the description's "Show" is consistent. The description adds useful context by specifying that the output concerns both the MCP platform and adapter versions, which is meaningful beyond generic annotation hints.

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 with no filler or repetition. Every word contributes to the meaning: it names the action, the resource, and the scope.

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 no-parameter read-only tool with no output schema, the description sufficiently states what the tool returns. It could add light contextual guidance, such as mentioning use in troubleshooting or bug reports, but that is more of a usage-guideline enhancement than a completeness 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 tool has zero parameters, and the schema is empty, so there is nothing for the description to explain or clarify. With 100% schema coverage (vacuously), the description's mention of "current" versions appropriately implies no parameters are 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 uses the specific verb "Show" and names the exact resource: "current MCP platform and adapter versions." It is unambiguous and clearly differentiates this tool from siblings like authenticate, connect, or report_bug.

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 tool's usage is implied by its purpose: it is the obvious choice when an agent needs version information. However, there is no explicit guidance about when to prefer this over alternatives such as toolkit_info, and no when-not-to-use conditions are stated.

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 meaningful context by specifying exactly what information is returned (installed MCPs, connection statuses, connected accounts, and catalog tool counts), which goes beyond the annotations and helps the agent anticipate the output.

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 entire description is a single, front-loaded sentence that begins with 'Returns' and immediately enumerates the key components of the toolkit state. There is no filler 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, no-parameter, read-only information tool, the description fully covers what the agent needs to know: the type of data returned and its granularity. No output schema exists, but the description sufficiently communicates the return content.

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 description coverage is trivially 100%, so there is no parameter information for the description to supplement. The baseline of 4 for zero-parameter tools 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 a specific verb ('Returns') and a clear resource ('current toolkit state') with enumerated details: installed MCPs, connection status, accounts, and catalog tool counts. This makes it easy to distinguish from sibling tools like connect, authenticate, 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 the tool is meant for inspecting toolkit state, but it does not explicitly state when to use it versus alternatives or mention any exclusions. No sibling tools are referenced, so the usage context is only implied by the read-only nature of the action.

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