Tribunal TRT20: Certidão Eletrônica de Ações Trabalhistas (CEAT)

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

The description goes beyond the annotations by explaining the browser login flow, the 'Authorization: Bearer <token>' config approach, and that the token is optional and session-scoped when passed directly. It does not contradict the idempotentHint=true or other annotations, and it adds useful behavioral context despite the annotations already hinting at safety.

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 compact three-sentence block with no filler. It front-loads the purpose and then lays out the two usage paths efficiently, though it is slightly dense and could be split into clearer steps.

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 simple schema and lack of output schema, the description covers the main behaviors and usage modes. It could mention what the tool returns after authentication or how errors are handled, but it is otherwise complete enough for an agent to invoke 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%, so the description must explain the 'token' parameter, and it does: it specifies the parameter is a JWT, that it can be pasted for a session-only login, and that omitting it returns the login link. This compensates well for the sparse 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 explains that the tool authenticates to MCP.AI by either returning a browser login link or accepting a pasted JWT. It identifies the resource (MCP.AI) and the action (authentication), though it doesn't explicitly distinguish itself from sibling tools like '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 usage guidance: users should prefer adding the token to the server config for a permanent connection, or call the tool with a token for session-only login, or call with no args to get the login link. This gives clear when-to-use instructions and describes the two main invocation patterns.

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 detail about conditional output—returning authenticated:true with empty pending[] versus connect_url when credentials are missing—which goes 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?

The description is two sentences, front-loaded with the main purpose, and every sentence earns its place. The conditional breakdown is compact and immediately useful.

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 no output schema, the description carries the responsibility of explaining return values, and it does cover the primary states: fully connected versus missing credentials. It does not mention possible partial-connection states or exact response structure, but for a parameterless read-only tool this is adequately 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 tool has zero parameters and schema coverage is 100%, so there are no parameter semantics to clarify. The baseline of 4 applies because the description correctly handles a parameterless tool without introducing irrelevant parameter information.

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 'Returns connection status and URLs,' a specific verb+resource statement that clearly identifies what the tool does. It distinguishes itself from the sibling 'authenticate' tool by reporting status rather than performing authentication.

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 conditional usage context: when all providers are connected it returns authenticated:true, and when credentials are missing it returns connect_url and per-install URLs. It does not explicitly name alternatives or exclusions, but the context is sufficient for a status-checking tool.

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?

Despite annotations being sparse (only flags), the description richly discloses behaviors: invoke runs uninstalled tools, returns connect or checkout links when needed, writes require owner/admin, search/describe show installation status, and uninstall/install have permanent effects. This adds substantial context that structured annotations do not 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 a very long, dense paragraph covering many features in a run-on style. While the content is valuable, it lacks structured formatting (bullets, sections) that would aid quick comprehension. It is far from minimal; every sentence earns its place conceptually, but the delivery is not crisp.

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 multi-action tool with no output schema, the description is remarkably thorough: it explains core flows, auth, payment, installation, prompt library, and edge cases like one-off invoke. Missing pieces are detailed parameter semantics and return formats, but overall it gives an agent enough context to operate effectively.

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 many action-related params (action, mcp_id, tool_id, arguments, prompt_slug, prompt_vars, etc.) and the flow linking them. However, several parameters (limit, query, immediate, tier_slug, cancel_reason, prompt_targets) are left undefined, leaving a significant gap for a high-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 identifies the tool as the official mcp.ai marketplace—a catalog and runner for MCPs/tools, plus a prompt library. It states a core flow (search → describe → invoke) and distinguishes itself from siblings by covering discovery, execution, installation, and prompt management. The verb+resource is implicit but very specific for a multi-action hub.

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 guidance on when to use each action: prefer invoke for one-off runs, install for permanent toolkit additions, list_tools for current callables, request_mcp when nothing fits. It also explains prerequisites (credentials, payment, admin rights) and contrasts inside-tool alternatives. This goes well beyond simple 'when to use this tool' and covers intra-tool action selection.

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, destructiveHint=false, and readOnlyHint=false. The description adds that conversation data is used 'for reproduction,' which is valuable context about how the tool operates. However, it doesn't disclose what side effects occur (e.g., creating a ticket) beyond what annotations imply. No 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 two sentences, front-loaded with the purpose and then a specific usage instruction. Every word earns its place; no fluff 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?

For a tool with only 3 parameters and no output schema, the description provides the essential context: what it does and the key requirement for reproduction. It could mention what happens after submission (e.g., response format), but given the tool's simplicity and the presence of annotations, it is sufficiently complete for an agent 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?

The schema has 0% description coverage, so the description must compensate. It explains the purpose of the 'conversation' parameter explicitly ('Include the conversation array with recent messages for reproduction'). The 'message' parameter is self-evident as the bug report content, and 'context' remains somewhat vague, but the primary parameter is addressed.

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 specific verbs and objects, and is easily distinguished from siblings like authenticate, connect, or marketplace, which serve entirely different 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?

It gives a clear instruction on how to use the tool: 'Include the conversation array with recent messages for reproduction.' While it doesn't explicitly mention when not to use or name alternatives, the context is obvious for a bug-reporting tool, and the instruction directly aids proper invocation.

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, covering the safety profile. The description adds no additional behavioral context such as response format, potential errors, or side effects. It is consistent with annotations and does not contradict them, but it adds no extra transparency beyond what annotations already 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 a single sentence: 'Show the current MCP platform and adapter versions.' It is concise, front-loaded with the action, and contains no redundant or extraneous information. 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?

For a simple version-information tool with no parameters and good annotations, the description is largely sufficient. It does not specify the return format (e.g., JSON structure), but this is a minor omission given the tool's simplicity and the clarity of 'versions.' An output schema is absent, so a bit more detail on the response could be helpful, but it does not detract significantly from 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?

The tool has zero parameters, so schema coverage is 100% and there is nothing to describe. Per the rubric, 0 parameters warrants a baseline of 4. The description correctly does not mention parameters, but also offers no supplementary guidance on inputs (none exist). 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 clearly states the tool's function: 'Show the current MCP platform and adapter versions.' The verb 'Show' and the resource 'MCP platform and adapter versions' are specific and unambiguous. It is easily distinguished from sibling tools like 'authenticate' or 'report_bug', which serve different purposes.

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 used when version information is needed, but it does not explicitly state when to use it versus alternatives or not to use it. For example, it doesn't mention that 'toolkit_info' might serve a similar purpose or any exclusions. This is acceptable but minimal—leaves usage context implicit.

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, so the safety profile is established. The description adds valuable context about what the read returns (installed MCPs, status, accounts, tool counts), which goes beyond annotation alone.

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 leads with the verb and resource, followed by a colon-delimited list of included details. Every word earns its place; no padding 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 zero-parameter read-only tool with strong annotation coverage. The description fully covers what the state contains, and no output schema is needed. Nothing essential is missing for an agent to invoke and interpret the result.

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 baseline is 4. The description does not need to explain parameters; the input schema is empty and fully documented.

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 begins with a specific verb 'Returns' and a concrete resource: the current toolkit state. It enumerates the exact details included (installed MCPs, connection status, connected accounts, catalog tool counts), clearly differentiating it from sibling tools like show_version 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 clearly implies when to use this tool: to inspect the overall toolkit state and connectivity. It doesn't explicitly mention when not to use it or name alternatives, but the context is clear enough for an agent to select it appropriately among 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?

Beyond the readOnly and idempotent annotations, the description discloses that the tool requires prepaid credits for each query, does not require platform credentials, and that it queries official non-confidential data while the client acts as data controller under LGPD. This adds meaningful behavioral 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 concise, consisting of a few clear sentences that cover the tool's purpose, hosting, payment, data source, and legal compliance. It is well-structured and avoids unnecessary elaboration.

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 the tool's function and legal context, but it does not mention the expected output format, potential errors, or any limitations (e.g., response structure, status codes). Given the lack of an output schema, this leaves the user without full context on what to expect from a successful or failed query.

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 lists cpf, cnpj, and nome as parameters, but the description provides no additional explanation about their usage, optionality, or interdependencies. It does not clarify whether these are alternative identifiers or if they collectively filter results, leaving room for 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 that the tool is for consulting electronic labor action certificates (CEAT) from the TRT20 court, specifying it as a query against an official source. This distinguishes it from the generic 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 indicates the tool is a query operation and mentions it is hosted by the platform, requires no platform credentials, and uses prepaid credits. However, it does not explicitly state when to use this tool over alternatives, such as when a user needs labor court certificate data, nor does it provide guidance on prerequisites or limits.

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