Tribunal TRT13: Certidão Eletrônica de Ações Trabalhistas (CEAT)

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

Annotations cover safety (idempotentHint: true, destructiveHint: false), and the description adds meaningful side-effect context: it can trigger a browser login flow, accept a JWT for session-scoped state, or return a link. The non-obvious session-vs-permanent distinction is well disclosed, and the text does not contradict the idempotent hint since repeated auth calls are benign. No annotation contradiction detected.

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?

Two information-dense sentences cover the essential content without wasted words. The 'best' vs. 'or' structure effectively separates the preferred permanent setup from the session fallback, though the first sentence runs on slightly and could benefit from a colon or semicolon. Overall, every phrase 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 single-optional-param authentication tool with no output schema, the description covers the critical aspects: the three invocation paths and their side effects on auth state. It effectively works around the absence of schema field descriptions. Minor gaps exist—such as no explicit statement of what the no-args call returns beyond 'get the link'—but these are largely inferable from the text.

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 carries the full burden of documenting the sole 'token' parameter. The description explicitly instructs 'call with { token: "<jwt>" } after the user pastes', which adds the format and usage context absent from the schema. While this compensates well for the missing schema documentation, some ambiguity remains about whether the token expects a raw JWT or other auth payloads, despite the JWT hint.

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?

'authenticate' clearly states the verb and resource: authenticating to MCP.AI. The description goes further to distinguish three invocation modes (permanent config, session token, link retrieval), each with distinct outcomes. This is a specific, unambiguous purpose that doesn't rely on the tool name alone.

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 scenarios: permanent config header for long-lived access, pasted token for session-only, and no-args to retrieve a login link. This effectively guides the agent on the appropriate invocation based on need, though it doesn't contrast these options against sibling tools like 'connect'.

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 meaningful behavioral detail beyond annotations by explaining the two return-state scenarios and the presence of per-install URLs, increasing transparency.

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 and every sentence adds value, explaining the tool's output and conditional behavior without redundancy 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 (no parameters, no output schema), the description is complete enough. It covers the main output fields (authenticated, pending, connect_url) and the two meaningful states, so an agent can invoke it without further clarification.

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 the baseline is 4. The description adds no parameter details, but none are needed since the tool takes no input.

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, with specific conditional behavior (authenticated:true when connected, connect_url when credentials missing). This distinguishes it from siblings like authenticate or marketplace by focusing on status retrieval rather than performing 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 contextual usage: it is appropriate for checking connection status and obtaining URLs when credentials are missing. It does not explicitly name alternatives or exclusions, but the condition-based behavior gives sufficient guidance for when to invoke it.

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 annotations by detailing runtime behavior: invoke works even without installation, returns connect link for missing credentials, and returns checkout link for insufficient wallet. It also explains the flag for installed_in_toolkit vs workspace and the admin requirement for writes. While not exhaustive (e.g., doesn't cover subscription renewal details), it adds substantial behavioral context 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 lengthy but well-structured, starting with a clear definition, then core flow, then key distinctions (invoke vs install), permissions, and finally the prompt library. It front-loads the most critical information and every paragraph adds unique value. While it could be slightly trimmed, the density is justified by the tool's complexity (23 parameters, multiple action modes).

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 scope (MCP catalog, execution, billing, prompt library) and the absence of an output schema, the description covers the main outputs: describe returns full profile, invoke returns connect/checkout links, get_prompt returns text with variables, publish_prompt returns a shareable link. It also explains permission requirements. It doesn't detail error handling or specific parameter combinations, but it provides enough for an agent to use the tool 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?

With 0% schema coverage, the description must compensate, and it does for key parameters: it explains action (search, describe, install, etc.), mentions tool_id in 'so you pick the right tool_id', and discusses prompt_vars in 'returns its full text with {{variables}} filled'. However, many parameters like mcp_id, message, immediate, tier_slug, and several prompt_* fields are not explained, leaving gaps for an agent trying to invoke specific actions.

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 then details the core flow (search→describe→invoke) and distinguishes between invoke and install, making the tool's role unmistakable. It also clearly separates from siblings like authenticate and connect by focusing on catalog, execution, and prompt library functionality.

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: 'Use install only to make an MCP PERMANENT in the active toolkit... prefer invoke for a single/occasional use.' It contrasts invoke vs install, explains when to use list_tools, and states 'request_mcp asks us to build a NEW MCP when nothing fits.' It also notes that writes require workspace owner/admin, giving operational boundaries.

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 idempotentHint=true and destructiveHint=false, so the description adds minimal extra behavioral info. It adds the context of requiring the conversation array, but does not explain what happens after submission (e.g., ticket creation). With annotations covering safety, this score is appropriate.

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 the first stating the primary purpose and the second providing actionable guidance on required inputs. No fluff, 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 reporting tool with no output schema, the description sufficiently covers purpose and key input guidance. It lacks detail on optional parameters and post-submission behavior, but given the tool's simplicity and the annotations, it is reasonably 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?

Schema has no descriptions for parameters, so the description must compensate. It explains the 'conversation' parameter explicitly, and implies 'message' is the report contentaa, but 'context' is not addressed at all. Partial compensation leads to a marginal 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 specifies the resource (bug/feedback) and the action, distinguishing it from siblings like authenticate or show_version which serve unrelated 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 gives clear guidance on what to include ('Include the conversation array with recent messages for reproduction'), which tells the agent how to properly use the tool. It does not explicitly mention when not to use it, but given the unrelated sibling tools, this is adequate.

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 fully covered. The description adds that the versions are 'current,' which is useful context. No other behavioral traits need disclosure for a simple version query.

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 wasted words. It is as concise as possible while fully conveying the tool's function.

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, no side effects, safe read-only operation), the description is complete. The absent output schema is not a gap because the purpose is unambiguous and standard for a version endpoint.

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 and schema both correctly indicate no inputs are required; there is nothing more to clarify.

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') with a clear resource ('current MCP platform and adapter versions'). It precisely states what the tool outputs and is distinct from sibling tools like authenticate or toolkit_info, which have 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 clearly implies the tool is for retrieving version information when needed. There are no similar sibling tools that compete for the same use case, so explicit alternatives are unnecessary. Context is clear, though no explicit when-not-to-use guidance is provided.

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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, establishing safety. The description adds transparency by specifying exactly what information will be returned (installed MCPs, connection status, accounts, catalog tool counts), which is useful behavioral context 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 sentence, immediately front-loaded with the verb 'Returns' and the resource. It lists the four key components without redundant filler, 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?

Given the simple nature of the tool, empty schema, and strong annotations, the description fully covers what an agent needs to know: the tool returns the toolkit state and what aspects are included. No output schema is present, but the description provides a clear inventory of return content, making it contextually 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 accepts no parameters (empty schema), so the description needs no parameter-level details. The baseline of 4 for zero-parameter tools applies; the description accurately reflects the parameterless nature.

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 'Returns' and clearly identifies the resource ('current toolkit state') with a detailed enumeration of its components (installed MCPs, connection status, accounts, tool counts). This distinguishes it from sibling tools like authenticate or show_version, making its purpose unmistakable.

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 for inspecting toolkit state, providing clear context about its use case. It does not explicitly exclude alternatives or mention when not to use it, but no competing tool serves this exact purpose. A 4 is appropriate for clear context without explicit exclusions.

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 annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds critical behavioral context: the tool is hosted by the platform, requires no platform credentials, is paid with prepaid credits, returns non-confidential official data, and makes the client (not the platform) the LGPD controller. This substantially exceeds the annotations' 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 compact single paragraph that front-loads the tool's purpose and then adds meaningful context about official source, pricing model, and legal compliance. Every sentence contributes value, though the LGPD disclaimer could have been shortened; overall the structure is efficient and 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?

The tool has three parameters, no output schema, and no parameter description, but it has strong behavioral context (credits, official source, legal roles). The main gap is the missing parameter semantics and the lack of any indication of return values. For a simple query tool, the current description is adequate but leaves some important operational details untouched.

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 provides only property names (cpf, cnpj, nome) with string types and zero descriptive coverage. The description does not explain how these parameters are used, whether at least one is required, how they relate to CEAT, or any validation constraints. Although the names are self-explanatory, the description fails to compensate for the 0% schema coverage, leaving the agent without critical parameter guidance.

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: consulting the CEAT (Certidão Eletrônica de Ações Trabalhistas) from TRT13 in an official source. It uses a specific verb 'consulta' and a specific resource, making it easy to distinguish from the sibling tools (authenticate, connect, 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 usage context is implied by the domain: pay-per-query consultation of official labor court data. However, there is no explicit guidance about when to prefer this tool over alternatives, nor any when-not-to-use guidance. The description explains cost and data sensitivity but does not describe competing tools or selection criteria.

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