Tribunal TRT8: Certidão Eletrônica de Ações Trabalhistas (CEAT)

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

Beyond the annotations, the description discloses meaningful behaviors: the browser login flow, the distinction between permanent and session-only access, and the no-args behavior that yields a link. It also explains the recommended header configuration. This adds substantial context that the annotations alone do not convey.

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 compact and front-loaded with the essential flow. It contains three related pieces of guidance in a logical sequence, and each sentence adds useful information. Slight wordiness in 'MCP.AI for IDE agents (Cursor, etc.)' prevents a perfect score, but overall it is appropriately sized.

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 authentication tool with one optional parameter and no output schema, the description provides enough context for an agent to invoke it: no args gets the link, token arg authenticates, and config header is the preferred long-term route. It could mention expected return values or error conditions, but these are not critical for basic invocation.

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 only a bare `token` string with 0% description coverage, so the description carries the full burden. It explains that `token` is the pasted JWT for session login and that omitting it triggers the login-link flow. This is meaningful added semantics, though it could elaborate on token format or failure behavior.

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: authenticating an IDE agent to MCP.AI by logging in via browser and supplying an access token. It provides concrete flow details (copy token, paste or configure as header), which is specific beyond just the tool name. It does not explicitly contrast with sibling tools like 'connect', so it falls short of a 5.

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 usage paths: add the token to server config for a permanent connection, paste it for session-only login, or call with no args to get the login link. It also clearly distinguishes between the two authentication modes, providing strong practical guidance for an agent deciding how to invoke the 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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral context by explaining conditional return states, including authenticated:true, empty pending[], and connect_url for missing credentials. It does not contradict the annotations and provides meaningful non-obvious detail.

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 sentences, front-loaded with the core purpose, then expands on conditional behavior. Every sentence adds relevant information without repetition or filler.

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, zero parameters, and helpful annotations, the description covers the main cases: all providers connected and missing credentials. It could be slightly more explicit about partial connectivity scenarios, but the information provided is sufficient for basic invocation and interpretation.

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 schema provides no parameter semantics to clarify. The baseline for no parameters is 4, and the description appropriately focuses on behavior rather than parameters. No additional param explanation 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 uses a specific verb and resource: 'Returns connection status and URLs.' It clearly defines the tool's output and distinguishes it from siblings like authenticate and show_version by focusing on status/URLs rather than performing an 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 clear context for when to use the tool by describing two states: all providers connected vs. missing credentials. It does not explicitly name alternatives or when not to use it, but the behavior is well-scoped enough that a user can infer this is the 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?

The description adds substantial behavioral context beyond annotations: invoke runs tools one-off without installing, returns connect or checkout links when credentials/payment are needed, and writes require workspace owner/admin. This matches the annotations and greatly enriches the agent's understanding.

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 front-loaded and dense with valuable information, with no filler. However, its single-paragraph structure makes it harder to scan; bullets or section breaks would improve parseability given the number of actions covered.

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?

It covers catalog discovery, one-off execution, installation, billing, bug reporting, MCP requests, auth requirements, and the prompt library. Gaps remain for the resume action, several schema parameters, and explicit return-value semantics.

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 and 23 parameters, the description compensates well for key fields like action, mcp_id, tool_id, and prompt-related parameters. However, many parameters remain unexplained, including limit, immediate, tier_slug, cancel_reason, cancel_comment, conversation, and request_details.

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 mcp.ai marketplace: a catalog of MCPs/tools and the mechanism to run them. It explicitly names the core actions and distinguishes this central marketplace tool from sibling tools like authenticate, connect, and report_bug.

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 an explicit core flow (search → describe → invoke), and directly advises when to use invoke versus install, with 'prefer invoke for a single/occasional use.' It also separates list_tools, subscribe/cancel, report_bug, request_mcp, and the prompt library functions.

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 safety profile (readOnlyHint false, destructiveHint false, idempotentHint true), so the description adds some context by instructing to 'Include the conversation array with recent messages for reproduction.' This is useful behavioral guidance, though it does not expand on side effects or message disposition. The description does not contradict 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 with no wasted words. It front-loads the primary purpose and adds a single actionable instruction about the conversation array. 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 reporting tool with 3 parameters and no output schema, the description covers purpose and the key usage note about reproduction. It does not explain return values, but these are likely unnecessary for a bug report submission. Adequate 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?

Schema description coverage is 0%, so the description must compensate. It clarifies the 'conversation' parameter by explaining it should contain 'recent messages for reproduction.' The required 'message' parameter is semantically implied by the tool purpose, and 'context' remains vague. Partial compensation earns a midpoint score.

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') and resource ('bug/feedback'), and it distinguishes itself from sibling tools like authenticate, connect, and 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?

The description provides clear context for when to use the tool: when there is a bug, missing feature, or feedback. It does not explicitly mention alternatives or exclusions, but the purpose is specific enough that users can infer when it applies, and the sibling tools are so different that no exclusions are needed.

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, which cover the safety profile. The description adds 'current' to indicate a live query but does not provide deeper behavioral context (e.g., format, latency, or data source). This is acceptable given annotations, but the description itself doesn't enrich beyond 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 a single sentence that conveys all necessary information without waste. It is front-loaded with the verb and resource, making it easy to parse quickly.

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 params, no output schema, low complexity), the description fully captures its function. It clearly states what version information is provided (platform and adapter), which is sufficient for an agent to understand the tool's purpose and expected 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, and the description correctly implies there is nothing to configure. No parameter ambiguity exists, so the baseline of 4 applies. The description adds no unnecessary parameter details.

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 clearly identifies the resource ('current MCP platform and adapter versions'). It distinguishes this from siblings like 'toolkit_info' by focusing specifically on version information, which 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?

The description implies usage when version information is needed but provides no explicit guidance on when to use this tool versus alternatives (e.g., 'toolkit_info'). No exclusions or decision criteria are given, leaving the agent to infer appropriateness.

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 value by detailing exactly what information is returned (installed MCPs, connection status, accounts, catalog tool counts), which is beyond the annotations and helps the agent understand the tool's scope.

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, well-structured sentence that front-loads the main action ('Returns the current toolkit state') and then lists the included elements. No wasted words, and it reads naturally. Perfectly concise.

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 query tool with no parameters and no output schema, the description fully explains what the tool returns. It is complete for the tool's complexity and leaves no important gaps for an agent to infer.

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 zero parameters, schema coverage is 100% and there is nothing for the description to clarify. The baseline for zero params is 4, and the description provides no misleading or redundant parameter information, so it fully meets expectations.

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 what the tool does: it returns the current toolkit state and specifies the exact components (installed MCPs, connection status, accounts, and catalog tool counts). This is specific and distinguishes it from sibling tools like 'connect' or 'marketplace' which perform different 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 does not explicitly state when to use this tool versus alternatives or mention any exclusions. It implies usage for checking state, but lacks direct guidance on when it is appropriate compared to siblings. The context is clear but not explicit about alternatives.

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/high-level hints, the description adds valuable behavioral and operational details: no platform credentials, prepaid per-query cost, the data is citizen-available official information, and LGPD/controller obligations. These go far beyond the annotation hints and are not contradicted by 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 compact and front-loaded with the core purpose, followed by operational details and a privacy note. Every sentence contributes: what it does, how it is access/payment, legal status of the data, and LGPD responsibility. No 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 read-only query tool with good annotations, the description covers source, access model, cost, confidentiality status, and LGPD implications. The main gap is parameter behavior, but that was already counted under parameter semantics; overall the tool is well contextualized for an agent to select and call 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 input schema has three string parameters (cpf, cnpj, nome) with 0% description coverage, and the tool description provides no parameter-specific guidance. It does not say whether at least one identifier is required, whether the parameters can be combined, or how to format them. The descriptive names are helpful but the description adds no semantic value on top of them.

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 a query for the CEAT from TRT8, a specific Brazilian labor court official source. The verb 'consultar' matches the action and the full name 'Certidão Eletrônica de Ações Trabalhistas' removes ambiguity. It is distinct from generic sibling tools like authenticate, connect, or 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 gives practical context for when and how to use it: official source, no platform credentials required, prepaid per-query credit, and the data is not confidential. It does not explicitly name alternative tools or exclusion cases, but the context is clear enough for tool selection given the sibling list.

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