IBAMA Autuações

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

The description reveals behavioral details beyond annotations: it explains the two login modes (permanent via config header, session-only via token), and that calling with no args returns a link. It does not state error behaviors or side effects, but with idempotentHint=true and destructiveHint=false, the safety profile is clear.

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 somewhat long and rambling, but each sentence adds critical steps. The structure could be tighter, but it is not wasteful; it provides necessary context.

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 low complexity (1 optional param) and no output schema, the description covers the essential flow: how to obtain a token, how to use it, and the difference between permanent and session-based login. It lacks explicit mention of return values or failure handling, but is otherwise 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 schema only defines an optional 'token' string with no description. The description fully explains its purpose: 'call with { token: "<jwt>" } after the user pastes' and 'with no args to get the link'. This adds significant meaning beyond the 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 states this is for authentication/logging in to MCP.AI for IDE agents, with explicit steps ('log in in the browser, copy the access token'). It distinguishes itself from siblings by focusing on the login flow, though it does not explicitly contrast with 'connect'.

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 provides concrete usage scenarios: 'Best: add it to this server's config...' for permanent login, or 'paste it here for a session-only login'. It also explains when to pass a token versus call with no args. It does not explicitly mention alternatives, but gives clear 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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable conditional return behavior: when connected, authenticated:true and empty pending[]; when credentials missing, connect_url and per-install URLs. This is 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 description is two sentences, front-loaded with the primary purpose, and each sentence adds meaningful detail about conditional outcomes. 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?

Despite having no output schema, the description specifies key return fields (authenticated, pending, connect_url) and the conditions under which they appear. For a zero-parameter status tool with strong annotations, this is sufficient for an agent to understand the tool's behavior.

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 schema coverage is 100% trivially. The description doesn't need to add parameter semantics. Per the rubric, a baseline of 4 is appropriate for 0-parameter tools.

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') with a clear resource ('connection status and URLs'). It also differentiates from sibling tools like 'authenticate' by clarifying it is a status check, not an authentication action.

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 implies this should be used to check connection status, and it specifies behavior under different credential conditions. However, it does not explicitly mention when to use alternatives or provide exclusions (e.g., 'use authenticate to initiate the flow').

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?

Beyond the annotations (readOnlyHint, idempotentHint), the description adds valuable behavioral context: it mentions prepaid credit payment, that the data is the same available to citizens, that it is not confidential, and that the customer is the data controller under LGPD. This goes beyond the safety profile already provided by 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, front-loaded with the purpose, and each subsequent sentence provides relevant operational or legal context. It avoids fluff, though the legal/payment details add length without overwhelming.

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 purpose, payment model, data source, and legal responsibilities, which is substantial for a simple query tool. However, it omits any explanation of the parameters or the response format, which is significant given the lack of an output schema and empty parameter descriptions.

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 elaborate on the parameters (ano, cpf, cnpj) at all. It neither explains what these fields represent nor how they are used, leaving the required 'ano' parameter entirely unexplained and the tool difficult to invoke correctly.

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 'IBAMA: Autuações Ambientais, consulta em fonte oficial', specifying the exact resource (environmental infractions) and the action (consultation). This clearly distinguishes the tool from siblings like authenticate or marketplace, which serve unrelated functions.

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: it is a paid per-query service, hosted by the platform, and requires no platform credentials. While it does not explicitly list when not to use it or name alternatives, the context makes the intended use case evident.

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?

Beyond the annotations (readOnlyHint=false, destructiveHint=false), the description discloses key behavioral traits: invoke runs tools one-off without installing them; invoke returns a connect link when credentials are needed and a checkout/top-up link when payment is required; install makes MCPs permanent; writes require workspace owner/admin; and publishing a prompt returns a shareable link. These details significantly extend the annotation information 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?

The description is long but dense and front-loaded: the first sentence states the tool's purpose, followed by the core flow, edge cases, and prompt library. Every sentence contributes meaningful guidance, and the length is justified by the tool's complexity (14 actions, 23 parameters). No filler or redundant phrases.

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 14 actions, 23 parameters, and no output schema, the description is remarkably complete. It covers workflows, permission requirements (owner/admin), payment/auth redirects, installed status flags, permanent vs one-off usage, prompt library publishing, and return link formats. There is enough context for an agent to select and invoke the correct action with 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?

With 0% schema coverage, the description takes on the burden of explaining parameters. It maps actions to their parameters well (action=search, action=describe, tool_id for invoke, prompt variables for get_prompt, etc.) and clarifies the flow of arguments. However, some parameters like immediate, resume, tier_slug, conversation, and report_context are never mentioned, and it does not state that arguments/prompt_vars are JSON strings. It compensates strongly for the main actions but not exhaustively for all 23 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 the tool as the official mcp.ai marketplace catalog and execution engine, with a specific verb+resource structure: it 'discovers MCPs by intent', 'describes' profiles, and 'invokes' tools. It also distinguishes itself from sibling tools by covering the marketplace actions (search/describe/invoke/install) and prompt library, which siblings like authenticate, connect, and show_version do 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 gives explicit when-to-use guidance: 'Use install only to make an MCP PERMANENT in the active toolkit; prefer invoke for a single/occasional use.' It also lays out the core flow (search → describe → invoke), and clarifies when to use list_tools, subscribe/cancel, report_bug, request_mcp, and the prompt library actions. It even describes fallback behavior: 'If nothing fits, request_mcp asks us to build a NEW MCP.'

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 idempotentHint=true, destructiveHint=false, and readOnlyHint=false, indicating a safe write operation. The description adds minimal behavioral context beyond the parameter instruction (include conversation array). It does not disclose side effects (e.g., whether a ticket is created, if external services are called) or any post-submission behavior, which would be valuable. No contradiction with 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 two sentences, front-loaded with the core purpose and a concise actionable instruction. There is no filler, and every word earns its place.

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 annotations cover safety aspects. However, the description leaves critical ambiguity around the 'conversation' parameter (array vs string) and does not mention prerequisites like authentication or what happens after submission. Since there is no output schema, return values aren't needed, but the missing format details reduce completeness.

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 explicitly explains the 'conversation' parameter (include recent messages) but does not clarify 'message' or 'context'. Additionally, the description calls it a 'conversation array' while the schema defines it as a string (default '[]'), creating ambiguity about the expected format. This is insufficient 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 tool's purpose: 'Report a bug, missing feature, or send feedback.' The verb 'Report' is specific and the three use cases are explicit. This clearly differentiates it from siblings like authenticate or marketplace, which have unrelated functions.

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 (bug, missing feature, feedback) and offers a specific instruction: 'Include the conversation array with recent messages for reproduction.' It does not explicitly mention alternatives or exclusions, but no other sibling tool serves this purpose, so the guidance is sufficient.

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 annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds that it shows platform and adapter versions, which is useful context, but it does not disclose any additional behavioral traits such as output format or whether network access is needed. Because annotations cover the main safety aspects, a score of 3 is appropriate.

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 unnecessary words. It front-loads the core functionality and is highly efficient, earning full marks for 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?

Although there is no output schema, the description tells the agent exactly what the tool returns (platform and adapter versions), which is sufficient for a simple version-check tool. It could have also mentioned the output format (e.g., text or structured data), but given the tool's simplicity and the absence of parameters, the description is largely complete. A score of 4 reflects the minor omission.

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 no parameters, so schema description coverage is 100% by default. The description does not need to explain any parameters. Per the rubric, a zero-parameter tool receives a baseline of 4, which is suitable here.

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 identifies the exact resource ('current MCP platform and adapter versions'). It clearly distinguishes this from other sibling tools like authenticate or connect, which serve different purposes such as authentication or querying environmental data.

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?

There is no mention of when to use this tool versus alternatives, no preconditions, and no exclusions. The description states only what the tool does, leaving the agent to infer that it is for checking versions. No explicit guidance is 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 and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by listing what state components are returned (installed MCPs, connection status, accounts, catalog tool counts), which is beyond the 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 a single, front-loaded sentence that immediately states the main action ('Returns the current toolkit state') and then lists the specific components. Every word adds value, with 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?

This is a simple, read-only, no-parameter, no-output-schema tool. The description fully captures what the agent needs to know: what the tool returns and its scope. There are no prerequisites, side effects, or error conditions to disclose. The description is complete for the tool's complexity.

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, and the schema is empty. The description doesn't need to explain parameters. Per the baseline rule for 0 params, this scores 4. The description correctly focuses entirely on the return value since there are no inputs.

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 specific verb 'Returns' and immediately specifies the resource: 'current toolkit state'. It enumerates the exact contents (installed MCPs, connection status, accounts, catalog tool counts), distinguishing it from sibling tools like authenticate or connect.

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 implicitly communicates when to use it (when you need toolkit state details) and the scope of information provided. It doesn't explicitly mention alternatives or exclusions, but the nature of the tool makes the use case obvious. The context is clear enough for an agent to select it appropriately.

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