Tribunal TRT1: Certidão Eletrônica de Ações Trabalhistas (CEAT)

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

The description discloses the tool's behavior for all three invocation paths, including side effects: it returns a login link when called with no args, completes login when given a token, and warns that tokens are non-expiring when used in config. While annotations declare idempotentHint=true, the description doesn't contradict this and adds context about session persistence and the recommended permanent setup.

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?

Concise and information-dense. Every sentence serves a distinct purpose: context for the target audience, recommended permanent approach, temporary fallback, and argument handling. The parenthetical about MCP.AI for IDE agents is short but crucial for understanding why this tool exists, and the forward-reference to config setup is well-placed.

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 1 optional parameter and no output schema, the description is complete: it covers all invocation modes, provides security context (non-expiring tokens), and explicit usage alternatives. The example of the token format is a nice touch. No additional information would meaningfully improve the agent's ability to use 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?

Schema coverage is 0%, but the description thoroughly explains the single token parameter's purpose and format: 'call with { token: "<jwt>" }'. It makes clear the parameter is optional and what happens when omitted. The description fully compensates for the lack of schema descriptions, though it doesn't specify validation rules like token format validation.

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 with a specific verb+resource combination: 'log in in the browser, copy the access token' and describes the three ways it can be invoked. It distinguishes itself as an authentication helper for the server, clearly separated from sibling tools like 'connect' and 'marketplace' by its focus on JWT-based session management.

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 and how to use the tool, including the recommended alternative: 'Best: add it to this server's config as a header `Authorization: Bearer <token>`. It also clarifies fallback usage for session-only login and no-arg invocation, covering all parameter combinations.

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 the description need not repeat those. It adds valuable behavioral detail about conditional outputs, such as authenticated:true versus connect_url responses.

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 just two sentences, front-loaded with the core purpose, and every sentence adds meaningful detail. There is no redundancy or fluff.

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 zero-parameter status tool, the description fully explains the return behavior in both scenarios. No output schema is present, but the description covers the essential conditional outcomes.

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 100% schema coverage, so the baseline is 4. The description appropriately focuses on behavior rather than parameters, which is sufficient for a no-argument 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 'Returns connection status and URLs', giving a specific verb and resource. It also explains distinct output conditions, which makes its purpose easy to distinguish from siblings like authenticate.

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 on when this tool is useful: to check connection status and obtain connection URLs when credentials are missing. It does not explicitly mention alternatives or exclusions, but the intended use is 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?

The description discloses important behaviors beyond the annotations: invoke works even when the MCP is not installed, returns connect/checkout links under auth/payment gaps, install/uninstall/subscribe/cancel require owner/admin, and the prompt library returns a shareable link that opens without login. Annotations only indicate readOnly=false and openWorld=true; the description adds rich operational detail 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 long but appropriately dense, front-loading the core flow and key invoke behaviors before covering auxiliary actions and the prompt library. It is structured as a continuous paragraph, which hurts scannability, but every sentence contributes meaningful information for a 14-action tool. A bulleted breakdown would be an improvement, but not a necessity.

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 high complexity (23 params, 14 action enum values, no output schema), the description covers an impressive amount: the end-to-end flow, one-off invoke, auth/payment edge cases, permission requirements, installed flags, and the separate prompt library. It omits specific return formats and some parameter details, but for a marketplace facade with no output schema, the coverage is nearly comprehensive.

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 23 parameters with 0% description coverage, so the description must compensate. It explains the key action= values and the roles of mcp_id, tool_id, arguments, prompt_slug, and prompt_vars within workflows. However, many parameters (limit, query, immediate, tier_slug, message, conversation, prompt_targets, request_details) are never explicitly described, leaving substantial gaps for a tool with such a large surface.

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 clear, specific statement: 'The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It also enumerates the distinct actions (search, describe, invoke, install, subscribe, etc.) and the prompt library, so an agent can easily understand the tool's scope and how it differs from siblings like report_bug 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 explicit when-to-use guidance: 'Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile... → invoke RUNS that tool.' It also explicitly says 'Use install only to make an MCP PERMANENT... prefer invoke for a single/occasional use' and distinguishes subscribe/cancel, report_bug, request_mcp, and list_tools. This goes well beyond minimum viability.

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=false, destructiveHint=false, and idempotentHint=true. The description adds the behavioral detail that the conversation array is needed for reproduction, which is useful context beyond the schema. However, it does not disclose what happens after submission (e.g., confirmation, response format) or any rate limits, but given annotations cover safety traits, this is acceptable.

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, each serving a purpose: stating the core function and providing a key usage hint. 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 bug-reporting tool, the description covers the essential action and the critical reproduction input. There is no output schema, so return value expectations are not defined, but that's not required for a simple feedback tool. Given the simplicity and annotations, the description is largely complete, missing only minor details like response 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?

Schema description coverage is 0%, so the description must compensate. It mentions 'conversation array' but does not explain the 'message' field (though clearly the required content) or the 'context' field's purpose. The description adds partial value but leaves gaps for two of three parameters, especially the optional '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 clearly states the tool's purpose: 'Report a bug, missing feature, or send feedback.' It uses a specific verb (report/send) and identifies the resources (bug, missing feature, feedback). This distinguishes it from siblings like authenticate, connect, or show_version, which have no overlap.

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 a direct usage hint: 'Include the conversation array with recent messages for reproduction.' This implies the tool is for reporting issues and that passing conversation context is important. It does not explicitly exclude other tools, but the siblings are clearly unrelated, so the context 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 cover read-only, idempotent, and non-destructive behavior. The description adds no extra transparency details (e.g., side effects, permissions). It is adequate but does not go 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 a single, succinct sentence with no redundant words. It is front-loaded with the action and resource, making it 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?

The description explains what the tool does (shows versions) and implies the output. It is complete enough for a simple query, though it does not specify the format or additional details. Given the low complexity, this is nearly sufficient.

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 the input schema is fully covered. According to the rubric, a baseline of 3 is appropriate when schema coverage is high, and the description adds no additional parameter information because none exist.

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: showing current MCP platform and adapter versions. It uses a specific verb 'Show' and identifies the resource, distinguishing it from sibling tools like 'toolkit_info' which may provide broader 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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention any conditions, prerequisites, or when not to use it, leaving the agent without explicit selection criteria.

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 value by specifying exactly what information is returned, which goes beyond the annotations and helps the agent understand the tool's output without guessing. 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?

A single sentence that front-loads the main verb ('Returns') and lists distinct output categories. Every phrase adds information, 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?

The description fully covers the tool's output scope in the absence of an output schema. For a read-only, parameterless tool, this is complete and self-sufficient—no hidden behaviors or missing details are evident.

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 100%. Per rubric, a baseline of 4 applies, and the description rightfully avoids inventing parameters. No additional parameter semantics 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, enumerating specific data types (installed MCPs, connection status, accounts, tool counts). It distinguishes itself from siblings like show_version, which likely only reports version info, and connect/authenticate which perform 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?

The description provides clear context for when to use the tool—whenever the agent needs an overview of the toolkit state. It does not explicitly name alternatives or exclusions, but the verb 'returns' and the listed contents make the use case obvious relative to action-oriented 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 declare readOnlyHint, idempotentHint, and destructiveHint, so the bar is lower. The description adds meaningful context about payment model, credential requirements, the public nature of the data, and the client's data controller role under LGPD. 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?

Three dense, purposeful sentences with no redundancy. The purpose is front-loaded, followed by operational and legal context. Every sentence 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 description covers source, access, payment, and legal compliance well. However, with no output schema and no parameter guidance, it leaves important usage gaps such as how to structure the query (CPF vs CNPJ) and what type of result to expect. This is a moderate gap for a consultation 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 mention cpf/cnpj at all. It fails to explain that queries are by CPF or CNPJ, whether both can be used, or which one is appropriate for CEAT queries. Users are left to infer from parameter names 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 it queries the official CEAT certificate from TRT1, naming both the tribunal and the specific resource type. This distinguishes it from generic sibling tools and leaves no doubt about its 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 gives clear operational context: official source, platform-hosted, prepaid credits, no platform credentials, and LGPD responsibilities. It does not explicitly compare with alternative tools or state when to prefer this one, but the context is sufficient for most cases.

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