Portal da Transparência: Cadastro de Expulsões da Administração Federal (CEAF)

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

Annotations indicate idempotentHint: true and destructiveHint: false, but the description adds critical behavioral context: it can establish either a permanent (non-expiring) connection via config or a session-only login. It also clarifies that calling with no args returns the login link. This goes beyond the annotations and sets expectations for both setup and session 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?

The description is a single sentence with multiple clauses, but it is front-loaded with the purpose and each clause adds essential information (login flow, two methods, no-arg behavior). It is not overly verbose given the complexity, though it could be broken into two sentences for readability.

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?

With only one optional parameter and no output schema, the description is complete: it covers the entire authentication flow, all invocation options, and the permanent vs. session distinction. It leaves no gaps about what the tool does or how to use it.

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 only a 'token' string with no description (0% coverage). The description compensates by explaining exactly when and how to use the token: paste it after the user provides it, or omit it to receive a login link. This adds significant meaning beyond the bare schema and covers both usage modes.

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: authenticate to MCP.AI for IDE agents. It specifies the action (log in), the resource (MCP.AI), and the intended audience (IDE agents like Cursor). It distinguishes itself from siblings by detailing the specific login flow (browser-based) rather than just a generic 'connect' or 'marketplace' 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 provides explicit instructions on when to use the tool: either for a permanent connection (via config header) or session-only (via token parameter). It also states the optional invocation patterns (with or without args). It doesn't explicitly contrast with sibling tools, but given the tool is self-contained, the usage guidance is clear and 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 declare readOnlyHint=true and idempotentHint=true, and the description adds specific output details (authenticated:true, pending[], connect_url, per-install URLs). It goes beyond annotations by describing response states, but does not cover partial connection scenarios or edge cases like mixed provider states.

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, then elaborates on output states. Every sentence adds meaningful information with no redundancy. It is 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?

For a simple read-only tool with no parameters, the description explains the main output states (all connected vs missing credentials) and what URLs are returned. It is mostly complete, but misses partial connection states and does not mention any prerequisites or potential errors. Given the low complexity, this is adequate 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, so the description does not need to explain parameter semantics. Per the guidelines, a baseline of 4 is appropriate for 0 parameters. The description focuses on output behavior rather than inputs, which is acceptable.

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, and explains the two key output states. It distinguishes itself from sibling tools like authenticate by focusing on status checking rather than authentication actions. The verb 'Returns' and resource 'connection status and URLs' are specific.

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 (to check connection status and get URLs), and implies scenarios like checking if credentials are missing. However, it does not explicitly mention when not to use it or name alternatives, though siblings like authenticate are distinct.

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 significantly extends the annotations by explaining one-off invoke behavior without installation, connect/checkout link handling, permission requirements for writes, and the prompt library's shareable links. 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 densely packed and well-organized: core flow, key invoke behavior, install vs invoke, permissions, then prompt library. Every sentence adds substantive guidance, and the structure makes the complexity manageable.

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 complex 14-action, 23-parameter tool with no output schema, the description covers the main flows, auth/payment edge cases, permission model, and prompt library. However, several actions and parameters (resume, immediate, tier_slug, cancellation fields, prompt publishing details) are not explained, leaving some gaps.

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 description coverage, the description must compensate, but it only explains a few key params (action, mcp_id, tool_id, prompt_slug). Most parameters (limit, immediate, tier_slug, conversation, cancel fields, request fields, prompt_targets, etc.) remain unexplained, leaving the agent to guess their meaning.

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 defines the marketplace as the mcp.ai catalog and execution engine, with a specific action flow (search → describe → invoke) and a separate prompt library. It distinguishes itself from sibling tools by being the in-platform catalog/runner rather than a specific utility 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?

Explicit guidance is provided: prefer invoke for one-off use, use install only for permanent toolkit additions, use list_tools for currently callable tools, and use request_mcp when nothing fits. It also states that writes require workspace owner/admin, giving clear when-to-use and when-not-to-use 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 readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: it does not require platform credentials, it is a prepaid per-query service, the data is not confidential, and the client is the data controller under LGPD. This goes beyond what annotations provide 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 concise and front-loaded with the core purpose, followed by relevant operational and legal caveats. Each sentence adds meaningful context, though the LGPD and billing details could arguably be condensed.

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 one-parameter, read-only query tool with no output schema, the description provides sufficient context: official source, non-secret data, payment model, and legal responsibility. It does not detail response format, but the lack of an output schema lowers the burden and the description still gives the reader a solid mental model.

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 one required parameter (cpf) with 0% description coverage, and the description does not mention CPF format, validation, or how the input is used. The description provides domain context (CEAF expulsion records) but does not compensate for the missing parameter semantics.

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 explicitly identifies the tool as a consultation of the CEAF registry on the official Portal da Transparência, using a specific verb ('consulta') and a specific resource. This clearly distinguishes it from the sibling platform tools like authenticate, connect, and 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 usage context: it is a hosted, read-only consultation of official Brazilian data, requires no platform credentials, and is paid for with prepaid credit. It does not explicitly name alternatives or exclusions, but the purpose is clear enough to guide selection among the unrelated sibling tools.

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 is not read-only, not destructive, and idempotent. The description adds minimal behavioral context—it mentions the need for conversation data for reproduction but does not disclose what happens after submission (e.g., ticket creation, external communication). No contradictions 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 long, front-loaded with the purpose and a key usage instruction. Every word adds value with no 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 simple 3-parameter tool with no output schema, the description explains the primary purpose and one critical parameter. However, it lacks guidance on the context parameter, message formatting, or what constitutes a successful submission, leaving some gaps for the agent.

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 mentions the 'conversation' parameter and its use, but does not clarify the 'context' parameter or elaborate on the expected format of 'message'. The conversation is described as an 'array' while the schema defines it as a string (likely JSON), creating ambiguity.

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: reporting a bug, missing feature, or sending feedback. It uses a specific verb (report) and resource (bug/feature/feedback), distinguishing it from siblings 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 provides actionable guidance by instructing the agent to include the conversation array for reproduction. However, it does not explicitly state when to use this tool versus alternatives, though the purpose itself implies the appropriate 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 declare readOnlyHint=true and idempotentHint=true, effectively conveying safety and idempotency. The description adds value by specifying that it returns both 'MCP platform and adapter versions,' which is not implied by the tool name alone. No contradiction exists, and the description enriches the annotation 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 a single, front-loaded sentence with no unnecessary words. It fully explains the function without waste.

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 simple tool with no parameters, no output schema, and clear annotations, the description covers all necessary context. It explains what versions are shown and is complete for its purpose.

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, so there is nothing to document. Per rubric, a 0-parameter tool receives a baseline of 4 since there is no parameter semantics to add.

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: 'Show the current MCP platform and adapter versions.' It uses a specific verb ('Show') and resource ('MCP platform and adapter versions'), and is distinct from sibling tools that handle auth, connection, marketplace, etc.

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 no explicit when-to-use or alternative guidance. However, the tool is simple and self-explanatory; usage is implied by the action word 'Show' with no exclusions or alternatives mentioned. This meets the 'implied usage' baseline.

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, destructiveHint=false. The description goes beyond by detailing the output content (connection status, accounts, catalog counts), which is valuable context not captured in structured metadata. However, it doesn't address potential side effects like network latency or caching, but for a read-only state query, this is sufficient.

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, well-structured sentence that front-loads the main action ('Returns') and then efficiently enumerates the included details. Every word adds value, and there is no redundancy or bloat.

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's simplicity (no parameters, no output schema), the description fully covers what the tool does and what it returns. An agent has all necessary information to decide when to call it and what to expect in the response.

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 empty. According to the rubric, 0 parameters warrants a baseline of 4. The description correctly implies no inputs are needed, reinforcing the tool's simplicity. No additional parameter explanation is necessary.

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 purpose: returning the current toolkit state, specifying exact facets (installed MCPs, connection status, accounts, catalog counts). This distinguishes it from siblings like 'authenticate' and 'connect' by focusing on introspection rather than actions.

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?

When to use is implied but not explicitly stated. The description doesn't mention alternatives or when not to use, but it's clear it's for querying state. Could be improved with phrases like 'use this to introspect' or 'instead of show_version, which only returns version info.' No harmful guidance, just implicit.

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