Tribunal TRT12: Certidão Eletrônica de Ações Trabalhistas (CEAT)

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

Annotations already indicate idempotent and non-destructive behavior. The description adds useful behavioral context beyond annotations, explaining the two login modes, how the token is supplied, and that no args returns a login link. It does not describe failure modes or side effects, but the added context is meaningful.

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 long, front-loaded with the core purpose, and every sentence contributes actionable information. It is concise without sacrificing necessary detail.

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 no output schema, the description covers the main workflows: permanent config via header, session login via token, and no-args link retrieval. It could mention success/error responses or invalid-token behavior, but overall it is sufficiently complete for an agent to invoke the tool 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 only defines an optional string token with no field description. The description fully compensates by explaining that 'token' is a JWT pasted from the browser, and that calling with no args yields the login link. This gives complete parameter semantics despite 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: authenticating an IDE agent to MCP.AI by logging in via browser and obtaining an access token. It gives specific actions and context, though it does not explicitly differentiate 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 clear usage guidelines: add the token to server config for permanent access, or pass it as a parameter for session-only login, or call with no args to receive the login link. It gives practical context but does not explicitly state when to prefer this tool over 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?

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral context beyond these: it specifies the exact structure of the response (authenticated flag, pending array, connection URLs) and how it varies based on provider connectivity. This goes beyond what annotations alone convey, without any 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 extremely concise: two sentences that immediately state the core function and then elaborate on the two key cases. There is no redundant wording, and the most important information (returns status and URLs) is front-loaded.

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 fully covers its behavior. It explains both possible outcomes and the data returned, leaving no significant gaps for an agent to infer. The annotations complement the description to make it 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, so the description does not need to explain parameter meaning. According to the rubric, a tool with 0 parameters gets a baseline of 4; the description does not add parameter-specific details, but none are 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: 'Returns connection status and URLs.' It also details the two distinct output states ('authenticated:true' with empty 'pending[]' vs missing credentials returning 'connect_url'), making its purpose unmistakable and distinct from siblings like 'authenticate' or 'toolkit_info'.

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 conditional scenarios ('When all providers are connected' vs 'When credentials are missing'), giving clear guidance on when the tool produces which output. However, it does not explicitly contrast with alternatives such as 'authenticate' or mention when to prefer this over other status-related tools, so the when-to-use guidance is implied rather than explicit.

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 are sparse (readOnlyHint=false gives no safety context), so the description carries the burden and excels. It discloses that invoke works even without installation, returns connect/checkout links on auth/payment issues, explains side effects of install (permanent toolkit addition), and specifies auth requirements for writes. No contradiction 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?

The description is a dense block of text spanning multiple paragraphs without bullet points, headers, or front-loaded key facts. It buries critical information (like invoke behavior) inside a long narrative. While length may be warranted given 23 params, the lack of structure makes it difficult to parse, and it is not appropriately sized for quick reference.

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?

Despite weak schema coverage and no output schema, the description is remarkably complete in explaining the tool's full behavior: all action variants, the search→describe→invoke flow, auth/monetization edge cases, and the separate prompt library. It covers both MCP and prompt capabilities, but omits detailed parameter mappings, which prevents a perfect score.

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%, and the description must compensate. It explains the core action parameters (action, query, mcp_id, tool_id, arguments) within the workflow, but does not systematically describe all 23 parameters (e.g., prompt_body, prompt_title, cancel_reason, report_context). It adds meaning for the primary actions but leaves many params unexplained, so coverage is partial.

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 unambiguously identifies the tool as the official mcp.ai marketplace for discovering and running MCPs, distinguishing it from sibling tools (report_bug, show_version, etc.) by explaining its core purpose and actions. It clearly describes what the tool does: search, describe, install, invoke, and manage MCPs and prompts.

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 explicitly provides usage guidance: it contrasts invoke vs install, stating 'prefer invoke for a single/occasional use' and 'use install only to make an MCP PERMANENT', and mentions list_tools for current capabilities, subscribe/cancel for billing, and report_bug for feedback. It also notes that writes require workspace owner/admin. This is exemplary when-to-use guidance.

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 provide idempotentHint=true and destructiveHint=false, so the description doesn't need to repeat that. The description adds that including the conversation array helps reproduction, which is a behavioral expectation beyond the schema. It 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 long, front-loaded with the purpose, and provides a key usage hint. Every sentence earns its place with no 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 tool is simple (3 params, 1 required), and annotations cover idempotency and non-destructiveness. However, the description doesn't explain what happens after reporting (e.g., no response, ticket creation) or how the conversation array should be formatted. For a bug-report tool, this is likely sufficient, but some completeness is lacking.

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% (context, message, conversation have no descriptions), so the description must compensate. It explains the 'conversation' parameter by advising to include recent messages for reproduction, which adds meaning. However, it doesn't clarify 'context' or the format of 'conversation' beyond the default, leaving some 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 the tool's purpose: 'Report a bug, missing feature, or send feedback.' This is a specific verb (report) with a clear resource (bug/feature/feedback). It is distinct from siblings like authenticate or marketplace, though it could further differentiate from generic feedback 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 indicates when to use this tool: to report bugs or missing features or send feedback. It also instructs to include the conversation array for reproduction, which provides practical guidance. However, it doesn't explicitly state when not to use it or alternative tools for other feedback types.

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 useful context by specifying that the tool reports both platform and adapter versions, going slightly beyond the structured annotations 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 a single, front-loaded sentence that conveys the tool's purpose without unnecessary words or repetition. 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 zero-parameter, read-only version information tool with strong annotations, the description is complete. It states what versions are shown and requires no further behavioral or return-value detail.

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, and the schema already confirms no inputs are required.

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 identifies the exact resource ('current MCP platform and adapter versions'). It clearly distinguishes this tool from sibling tools like authenticate or report_bug by focusing on version retrieval.

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 use when version information is needed, but it does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. For a simple version-check tool, the implied usage is reasonably 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds valuable context about what specific information is returned, which goes beyond the annotations 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 a single, well-structured sentence that front-loads the core action and then provides a concise list of the returned data. Every word adds value, with no 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?

For a parameterless read-only status tool, the description is complete: it names the return categories and implies no side effects. No output schema exists, but the description sufficiently explains what the agent will receive.

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 fully covers parameter semantics. The description does not need to add parameter details; the baseline of 4 applies because there is nothing missing.

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 identifies the exact resource ('current toolkit state'), then enumerates the returned contents: installed MCPs, connection status, accounts, and catalog tool counts. This clearly distinguishes it from action-oriented siblings 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 makes the tool's role as a read-only status/inspection tool clear, implying it should be used when an agent needs to understand the current toolkit state. It does not explicitly state when not to use it or name alternatives, but the context of sibling tools makes the usage context evident.

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 annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false), the description discloses important behavior: hosted by the platform, no platform credentials, pay-per-query with prepaid credit, and consultation of non-confidential official data. It also provides LGPD data-controller responsibilities, adding substantial context beyond what annotations 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 core purpose, followed by essential operational and legal context. Every sentence contributes meaningful information: purpose, payment/credentials, data source, confidentiality, and LGPD responsibility. 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?

The description covers purpose, payment, and legal context well, and annotations cover safety semantics. However, with no output schema and no explanation of response format, error behavior, or how the three optional parameters should be supplied, an agent still faces ambiguity when actually invoking the 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%, but the description does not explain how to use cpf, cnpj, or nome, nor whether at least one is required or in what combinations. The field names are self-explanatory, but the complete absence of guidance on required identifiers or selection criteria is a notable gap.

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?

Description uses a specific verb (consultar) and clearly names the resource: Certidão Eletrônica de Ações Trabalhistas (CEAT) at Tribunal TRT12. It clearly distinguishes this from the generic sibling tools like authenticate, connect, and marketplace by identifying the exact official consultation purpose.

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 indicates when this tool is appropriate: for official CEAT queries from Brazilian official sources. It adds practical context such as prepaid credit and no platform credentials, but it does not explicitly mention alternatives or exclusions, so it falls short of a 5.

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