Tribunal TRT4: Certidão Eletrônica de Ações Trabalhistas (CEAT)

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

Annotations already indicate idempotentHint=true and non-destructive. The description adds operational details: returns a login link when called with no args, accepts a JWT token for session login, and explains the permanent header approach. No contradiction with annotations, and the added context helps predict 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 bit longer than strictly necessary but every sentence carries useful information: context, recommended setup, and invocation patterns. It front-loads the purpose and then details usage, making it scannable.

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 tool with one optional parameter and no output schema, the description covers both authentication flows and the config alternative. It doesn't explicitly state the return value when called with token, but the context makes it clear the tool facilitates login. Overall, no major gaps for the agent's decision-making.

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 has one optional string token with no description coverage. The description explains the token is a JWT pasted from the browser for session-only login, adding crucial meaning beyond the bare schema. This compensates for the 0% schema coverage.

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 server by logging in via browser and capturing an access token. It distinguishes itself by specifying the target audience (IDE agents) and the two auth modes. Even without naming siblings, the purpose is unambiguous.

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: recommends the permanent header-based config as best practice, explains session-only login via pasted token, and instructs when to call with no args vs with token. This gives clear when-to-use and alternative pathways.

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 read-only, idempotent, and non-destructive. The description adds value by detailing conditional response structures (e.g., authenticated:true, pending[], connect_url) and what triggers different fields, going beyond the safety hints 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 three concise sentences with no fluff. It front-loads the main purpose and then efficiently explains response variations, with every sentence contributing useful 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 parameterless status tool, the description covers the two most important cases (all connected vs. missing credentials) and key fields. It could be more thorough by mentioning potential other response fields or error scenarios, but it's largely sufficient given the tool's simplicity.

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 no parameters, and the schema is empty. Per rubric, 0 params earns a baseline of 4—the description doesn't need to add parameter-level detail, and it doesn't.

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,' using a specific verb and resource. It distinguishes itself from siblings like 'authenticate' by focusing on status, not actions, and is not a tautology.

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 when to use this tool (to check connection status) and describes response conditions, but it does not explicitly mention alternatives or provide 'when not to use' guidance. The usage is inferred rather than directly stated.

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 goes far beyond the minimal annotations (readOnlyHint=false, etc.) by detailing side effects: invoke runs even if the MCP is not installed and does not add it to the toolkit, install makes it permanent, writes require owner/admin, and payment/auth flows involve connect/checkout links that require retry. There is no contradiction with annotations—the write capability is appropriately described.

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 dense paragraph without bullet points or logical breaks, making it hard to scan. While every sentence adds value, the lack of structure reduces readability. It could be improved by grouping related actions (e.g., marketplace operations vs. prompt library) into separate sections.

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 complexity (23 parameters, no output schema, multiple sub-actions), the description is remarkably comprehensive. It covers the full action set, expected returns (e.g., describe returns profile with tools/pricing/auth, invoke returns connect/top-up links), permission requirements, and the prompt library—all in one coherent narrative. No significant context is missing.

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 description coverage, the description carries the burden of explaining parameters. It effectively covers core parameters like action, mcp_id, tool_id, arguments, and the prompt_* fields, linking them to the narrative flow. However, some parameters (limit, immediate, tier_slug, cancel_comment) are left unexplained, leaving minor gaps despite the overall strong contextual mapping.

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 tool as the official mcp.ai marketplace, detailing its role as a catalog and execution engine for MCPs and prompts. It names specific verbs (search, describe, invoke, install) and the resource (MCPs/prompts), and it distinguishes itself from siblings by being the central hub that handles discovery and execution.

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, including the core flow (search→describe→invoke), when to prefer invoke over install ('prefer invoke for a single/occasional use; use install only to make an MCP PERMANENT'), and when to use other actions like list_tools, subscribe, and report_bug. It also clarifies write permission requirements (owner/admin) and behavior when credentials or wallet are missing.

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?

No contradiction with annotations; idempotentHint=true is aligned with reporting being non-side-effect-free, though it adds no explicit behavioral description of what happens after reporting. The description provides context about including conversation for reproduction, which adds value beyond 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 one sentence, concise and to the point, immediately stating purpose and a key usage tip. It is efficient with no filler, earning a high score.

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 with no output schema and few parameters, but the description only covers reproduction context, not other use cases like feedback. Given its simplicity, the description is adequate but could clarify when to use 'context' or how the conversation should be formatted.

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' and 'message' implies reporting content, but does not clarify 'context' parameter. With 3 params and 0% coverage, baseline is low, but description gives partial guidance, resulting in a 3.

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 states the tool reports a bug, missing feature, or feedback, which is a clear purpose. It does not differentiate from sibling tools since none are similar, but the scope of 'report a bug' is specific enough.

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 says to include a conversation array for reproduction, which implies use when reporting issues with conversation context. It does not explain when NOT to use it (e.g., feedback only context) or alternatives, so guidance is limited.

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, covering the safety profile. The description adds no extra behavioral context such as return format or side effects. It is adequate but does not go beyond 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 sentence, front-loaded with the purpose, and contains no superfluous information. It is appropriately concise for a simple version-check 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?

Given the tool's simplicity (no parameters, no output schema, and annotations covering safety), the description fully delivers the needed context. It states exactly what version information will be shown.

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 no parameters, so the baseline for 0 parameters is 4. The description adds no parameter information, but none 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 the action (show) and resources (MCP platform and adapter versions), making it easy to distinguish from sibling tools like authenticate or connect. It is specific and unambiguous.

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 when version information is needed, but it does not explicitly state when to use it or mention any alternatives. There are no exclusions or explicit guidance, so usage is only implied.

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 safety. The description adds no new behavioral traits beyond enumerating the information returned. It does not contradict annotations, so a baseline of 3 is appropriate given the strong annotation coverage.

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 packs all relevant details without fluff. It is front-loaded with the key action and subject, then enumerates the specific data points returned. 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?

With no output schema, the description carries the burden of explaining what the caller can expect. It does so thoroughly, listing four distinct aspects (installed MCPs, connection status, accounts, catalog tool counts). For a tool with zero parameters and minimal complexity, this is complete enough for an agent to decide and use.

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 no parameters, and the input schema is empty (schema coverage 100%). Per the baseline, a 4 is given for 0-parameter tools, and the description correctly makes no parameter claims. It does not need to explain anything about parameters since 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 verb 'Returns' and the resource 'current toolkit state', specifying exactly what is included (installed MCPs, connection status, accounts, and catalog tool counts). This distinguishes it from siblings like show_version, which likely provides only version info, and from the other tools 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 implies this is the go-to tool for checking the overall state of the toolkit, but it does not explicitly contrast it with alternatives like show_version or marketplace. However, the context is clear enough that an agent would understand to use this when needing a status overview.

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 goes beyond the annotations by disclosing that no platform credentials are required, that per-query prepaid credits are needed, and that data is non-confidential and the client is the data controller under LGPD. These are important behavioral aspects not covered by the readOnly/idempotent 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 fairly concise but has some redundancy (e.g., 'fonte oficial' appears twice) and could be streamlined. The main purpose is front-loaded, and it covers key operational details without excessive length.

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 lacks essential context: it does not specify what the tool returns (e.g., certificate details, success/failure), nor does it explain parameter dependencies (e.g., whether CPF or CNPJ is required). While it provides some constraints (payment, data control), it is incomplete for a query tool without an output schema.

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 three parameters (cpf, cnpj, nome) with no descriptions, and the tool description does not explain their meaning, usage, or which are required. With 0% schema description coverage, the description must compensate, but it fails to do so, leaving parameters ambiguous.

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: consulting Electronic Labor Action Certificates (CEAT) from TRT4. It is specific about the resource (CEAT) and the court (TRT4), and it distinguishes itself from sibling tools (authenticate, connect, etc.) which are not query tools.

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 about the tool's use (official source, per-query payment) but does not explicitly mention when to use it versus alternatives. However, since the sibling tools are not similar in function, the lack of explicit exclusions is less critical. It could benefit from stating that it is the sole query tool for this data.

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