SEFAZ MG: Débitos de IPVA

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

Beyond annotations, the description discloses that the token can be stored permanently via configuration, that pasting it gives session-only access, and that no-args call returns the login link. It does not fully describe error or expiration behavior, but it adds meaningful behavioral context beyond the structured metadata.

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, using two dense sentences with inline examples. Every sentence adds unique information about permanent configuration, session-onlyusage, or invocation shape, with no 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 small auth flow with one optional parameter and no output schema, the description covers invocation variants and setup context well. It lacks an explicit statement of what the tool returns on success/failure, but the core selection and invocation requirements are fully covered.

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 only a token string with no description and 0% documented coverage, so the description carries the full burden. It fully explains the token's format (JWT), that it is optional, what happens when omitted, and how to pass it after the user pastes it.

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 is for authenticating to MCP.AI, with two concrete flows: log in via browser and use an access token; with no args to get the link or with a token to complete a session login. It adds real scope beyond the bare verb 'authenticate' and distinguishes the permanent config-header path from the session-only paste path.

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: best permanent method is adding a header to server config, session-only login is achieved by pasting a JWT, and calling with no args yields the login link. This gives an agent clear conditional paths for when to call the tool and how to structure the call.

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, so safety is covered. The description adds value by explaining what is returned in different states (authenticated:true/pending vs connect_url), which is behavioral context beyond annotations. No contradictions.

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-loads the core function, and uses conditional phrasing to provide essential detail without wasted words. Highly 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?

Given no output schema, the description sufficiently explains the main return values (authenticated, pending, connect_url) in key scenarios. It is complete for a read-only status tool with no parameters and good annotations, though it could mention error or failure cases.

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, and the schema is fully covered with no properties. The baseline for 0 params is 4, and no additional parameter explanation 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 it 'Returns connection status and URLs', specifying the verb and resource. It distinguishes from siblings like authenticate by focusing on status rather than performing authentication, and provides detailed conditional behavior.

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 checking connection status (e.g., before authenticating) but does not explicitly contrast with alternatives like authenticate. It provides situational context (when providers are connected vs missing credentials) but lacks explicit when-to-use 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 beyond the annotations: invoke runs uninstalled MCPs one-off without bloating the toolkit, missing credentials produce a connect link, missing payment produces a checkout/top-up link, and writes require workspace owner/admin. No contradiction with the annotations exists.

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 front-loaded, covering the catalog definition, execution semantics, billing/auth behavior, permissions, and prompt library actions. It is not as scannable as bullets would be, but nearly every sentence contributes operational 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 such a broad dispatcher with no output schema, the description covers the main execution paths, permission requirements, auth/payment fallbacks, and prompt library well. It is not fully complete because several parameter groups and edge behaviors remain unexplained, but it is substantially more than the minimum.

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 23 parameters and 0% schema coverage, the description must compensate heavily. It usefully explains the action parameter and the key mcp_id/tool_id workflow, but many parameters like arguments, immediate, tier_slug, conversation/detail fields, and prompt fields remain only inferable from their names rather than explained.

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 immediately identifies the tool as the official mcp.ai marketplace: the in-platform catalog of MCP/tools and the way to run them. It clearly differentiates the core flow (search → describe → invoke) from the prompt library and auxiliary actions, so the agent knows exactly what this tool is for.

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?

Usage guidance is explicit and decision-oriented: 'use install only to make an MCP PERMANENT', 'prefer invoke for a single/occasional use', 'list_tools lists what is callable right now', and subscribe/cancel, report_bug, request_mcp, and prompt actions are each assigned a distinct purpose. This gives the agent clear when-to-use and when-not-to-use 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 provide idempotentHint=true, readOnlyHint=false, and destructiveHint=false. The description adds a meaningful behavioral instruction: the conversation array is to be included for reproduction. However, it doesn't disclose what happens after submission, whether the report is sent externally, or how idempotency is maintained.

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 contains only two sentences, with the first giving an immediately clear purpose and the second giving a practical instruction about the conversation parameter. Every word earns its place, and it is appropriately sized for this simple 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 feedback-reporting tool, the purpose and one special parameter are conveyed well, and annotations cover behavioral constraints. But the description lacks any explanation for the 'context' parameter, doesn't mention expected feedback after submission, and leaves the 'conversation' array formatting entirely to the user's imagination.

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 no property descriptions (0% coverage), so the description must compensate. The only parameter it meaningfully explains is 'conversation' ('recent messages for reproduction'). The required 'message' parameter is only inferable from the tool's purpose, and 'context' is 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 uses a specific verb ('Report') and lists a clear resource scope ('a bug, missing feature, or send feedback'). This clearly differentiates it from sibling tools like authenticate, connect, and show_version, establishing its unique 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 clearly indicates it is for bug reporting, feature requests, and feedback—contexts where it should be used. It does not explicitly mention when not to use it, but the sibling list shows no overlapping alternatives, so the usage context is clear without needing exclusions.

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 false. The description adds valuable behavioral context beyond annotations: no platform credentials required, prepaid credit pay-per-query, official/non-confidential data source, and LGPD responsibility. It does not mention rate limits or response details, but the annotation coverage lowers the bar.

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 main purpose is front-loaded in the first sentence. The subsequent sentences add relevant context about hosting, payment, and legal compliance, though the LGPD statement is slightly boilerplate-heavy. The description is compact and not 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?

The description covers source, payment, and data-privacy context well, but it does not describe what the response contains (e.g., debt amounts, due dates) nor the parameter semantics. Since there is no output schema, the description should provide more detail about expected results, making it incomplete for a paid query 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?

Schema description coverage is 0%, and the description does not explain the role of renavam, cpf, or cnpj, nor the required/optional relationships beyond what the schema states. The parameter names are self-evident, but the description fails to compensate for the missing schema 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's verb and resource: 'consulta' for 'Débitos de IPVA' via 'SEFAZ MG'. It also distinguishes the tool from the generic sibling tools by naming the specific official Brazilian source.

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 operational context: the query is against an official source, the same data available to citizens, and the client is responsible for legitimate LGPD use. It does not explicitly mention alternatives or exclusions, but the context is sufficient for an agent to decide when this tool applies.

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 clearly mark this as read-only (readOnlyHint: true), non-destructive (destructiveHint: false), and idempotent (idempotentHint: true). The description matches these by stating it only 'shows' information without implying side effects. This fully discloses behavioral implications.

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 efficiently conveys the tool's function. It includes the necessary information without verbosity, adhering to the principle of conciseness.

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?

While the description and annotations are clear, the absence of an output schema leaves callers unaware of the exact response structure. However, given the simplicity of the tool and its read-only, information-providing nature, the current description is mostly complete. A small gap exists in specifying the output 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?

The tool has zero parameters, and the schema confirms this with an empty properties object. There are no parameter details to document, so the description covers all parameter semantics adequately.

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 'Show the current MCP platform and adapter versions' clearly identifies the tool's purpose and output topic. It is specific and unambiguous, stating exactly what information is exposed.

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 has no parametersched input, so there are no usage prerequisites. Its purpose is self-evident; a version query requires no special conditions. The sibling tools (authenticate, connect) might be prerequisites, but the description does not explicitly mention them, which is acceptable for a simple query.

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, destructiveHint=false, and idempotentHint=true, covering the safety profile. The description adds value by detailing what the returned state includes, which helps the agent anticipate the response structure beyond the annotation metadata.

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 sentence, front-loaded with the verb and resource, then a colon-separated list of contents. Every word earns its place; there is no fluff 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?

The tool is simple (no parameters, no output schema), and the description fully specifies the return components: installed MCPs, connection status, connected accounts, and catalog tool counts. No critical behavioral or output information is missing for a read-only info 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 trivially 100%. With no parameters to describe, the description need not add parameter semantics; 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 clearly states the action ('Returns') and the resource ('current toolkit state'), and enumerates the specific components returned (installed MCPs, connection status, accounts, catalog tool counts). This distinguishes it from sibling tools like show_version, which likely focuses on just the 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 implies this is for inspecting overall toolkit state, but it does not explicitly mention when to prefer it over sibling tools like show_version or connect. No exclusions or alternative guidance are provided, though the purpose is clear enough for basic inference.

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