Tribunal TST: CNDT

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

The description discloses behavioral aspects beyond annotations, such as persistence (permanent vs. session) and the need for user input (pasting token). It doesn't explicitly mention potential side effects, but annotations already indicate it is idempotent and not read-only, so the description adds useful context without 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 concise and well-structured, fitting into three sentences. It avoids redundancy and clearly separates the two use cases. The structure flows logically from general purpose to specific instructions.

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 is complete enough for the tool's purpose. It explains the authentication process and the two ways to use it, but does not specify the return value or what happens after successful login (e.g., whether a session is established). This is acceptable given there is no output schema, and the tool name 'authenticate' makes the outcome implicit.

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 sole parameter 'token' is explained as a JWT, and its optional nature is clarified by describing two calling patterns (with token for session login, without for obtaining the link). This adds meaningful detail beyond the schema's basic type and optionality.

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: authentication for MCP.AI IDE agents. It explains the two main workflows (permanent config vs. session login) and is unambiguous about the action (log in, get token).

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 guidance on when to use each mode: 'Best: add it to this server's config... for a permanent, non-expiring connection' and 'Or paste it here for a session-only login.' It also explains the argument variations (with token vs. no args).

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 and idempotentHint=true. The description adds valuable behavioral context by specifying the exact return payloads: authenticated:true and empty pending[] when all connected, and connect_url when credentials are missing. This explains what the agent will receive, which goes beyond the annotation hints. It does not contradict any 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, front-loaded with the core purpose. Every sentence provides useful information without redundancy. It is concise and well-structured, with no fluff or unnecessary 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?

Given the low complexity (0 params) and the presence of annotations detailing safety, the description adequately explains the return values. It covers the two main states (all connected vs. missing credentials) and what the response includes. While it does not detail the exact structure of pending[] or connect_url, it provides sufficient context for an agent to understand the tool's output 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 tool has 0 parameters, so the description does not need to explain any parameter semantics. The baseline for 0 params is 4, and the description does not need to add any parameter-specific information. The absence of parameters makes this dimension non-applicable beyond the baseline.

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: 'Returns connection status and URLs.' This is a specific verb (returns) with a clear resource (connection status and URLs). It also distinguishes from siblings like 'authenticate' by focusing on status retrieval rather than initiating authentication, and provides specific conditional behaviors (connected vs. missing credentials).

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 it (when you need to check connection status) but does not explicitly mention alternatives or exclusions. It notes different return conditions based on credential status, which gives context, but there is no direct guidance on 'use this instead of authenticate' or 'use this when you need a read-only status check.' Thus, it relies on implied usage.

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=false, openWorldHint=true), the description discloses key behaviors: invoke runs tools one-off without adding to the toolkit, returns connect/checkout links when credentials/payment are needed, and writes require workspace owner/admin. It also explains the installed_in_toolkit vs installed_in_workspace flags and the fact that invoke includes a hidden one-off install. This adds substantial actionable 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 long but every sentence adds unique, non-redundant value. It is front-loaded with the core purpose, then flows into key distinctions (invoke vs install), permission notes, and the prompt library. It could be slightly more concise, but the density is justified given the tool's complexity. The structure is logical and 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?

For a tool with 23 parameters, no output schema, and multiple sub-actions, the description is remarkably complete. It covers the full workflow, behavioral edge cases (credentials, payment), permission model, the prompt library, and even gives examples of capability requests. It does not detail every return value, but the core outcomes are stated (e.g., describe returns full profile, invoke returns connect/checkout link). This ensures an agent can operate 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 23 parameters and 0% schema description coverage, the description carries a heavy burden. It mentions the action enum implicitly via the flow (search, describe, invoke) and explains that describe returns tool id + params, pricing, auth, but it does not explain individual parameters like limit, query, immediate, tier_slug, prompt_vars, etc. Some parameters are self-evident from the schema, but many subtle ones (immediate, tier_slug, prompt_targets) remain undocumented. The description provides a conceptual layer but not parameter-level semantics.

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 is the official mcp.ai marketplace for discovering and running MCPs/tools, and it enumerates specific actions (search, describe, invoke, install, etc.). It distinguishes itself from sibling tools by outlining the core search→describe→invoke flow and the prompt library. The verb+resource pairing 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?

It explicitly provides when-to-use guidance: 'prefer invoke for a single/occasional use', 'Use install only to make an MCP PERMANENT in the active toolkit', and describes alternatives like list_tools for what is callable now. It also notes permission requirements for writes (workspace owner/admin) and differentiates MCP vs prompt library usage.

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 annotation already indicates this is a write operation (readOnlyHint=false) and non-destructive. The description adds the behavioral nuance that the conversation array is used for reproduction, which is useful. However, it does not disclose what happens after reporting (e.g., ticket creation, notifications) or any side effects, leaving a moderate gap.

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 concise, well-structured sentences. It front-loads the purpose and adds the key usage detail about the conversation array. No extraneous 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?

For a simple tool with three parameters and no output schema, the description covers the core purpose and one parameter's role. It lacks an explanation of the context parameter and does not describe expected results or post-submission behavior. While sufficient for basic use, it leaves some gaps.

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 description explains the purpose of the conversation parameter ('with recent messages for reproduction') and implicitly describes the message parameter as the bug/feedback content. However, the context parameter is completely unaddressed, and with 0% schema description coverage, the description does not fully compensate for all three parameters.

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-resource combination that distinguishes it from sibling tools like authenticate, marketplace, and toolkit_info, which serve 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 implicitly defines when to use the tool: whenever there is a bug, missing feature, or feedback to report. It also provides a usage hint by instructing to include the conversation array. However, it does not explicitly name alternatives or state when not to use it, which prevents a 5.

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 specificity about what is displayed (platform and adapter versions) but offers no additional behavioral context such as output structure or error conditions. This matches the baseline for annotation-supported tools.

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 concise sentence with no filler or redundancy. It immediately and efficiently communicates the tool's purpose, earning the highest score for conciseness.

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 strong annotations—the description is sufficiently complete. It tells the agent exactly what the tool does, and nothing more is needed for a version check operation.

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 description coverage is trivially 100%. There is no need for the description to explain parameter semantics, and the baseline score of 4 is appropriate given the absence of parameters.

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 the specific verb 'show' and identifies the precise resource ('current MCP platform and adapter versions'), making its purpose unambiguous. It clearly distinguishes itself from sibling tools like authenticate, report_bug, and marketplace, which serve 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 no guidance on when to use this tool versus alternatives. It simply states what it does, leaving the agent to infer usage. There are no mentions of when to invoke it, when not to, or which sibling tools might be more appropriate for related tasks.

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 is consistent with annotations (read-only, idempotent) but does not add extra behavioral insights beyond what annotations already cover. It doesn't mention side effects, rate limits, or other traits that could affect agent decisions.

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, clear sentence with no redundant information. It efficiently conveys the tool's purpose without unnecessary details.

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 simplicity of the tool (read-only status retrieval), the description provides sufficient detail about what state it returns. It doesn't specify the exact output structure, but that may not be necessary for this low-complexity 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?

The tool has no parameters, so there is nothing to describe. The schema is empty, and the description does not need to elaborate on parameter usage.

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: returning the current toolkit state with specific details, using a specific verb ('Returns') and resource ('toolkit state'). It distinguishes from sibling tools like 'authenticate' or 'connect' by focusing on a read-only status overview.

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 no guidance on when to use this tool versus alternatives. It lacks explicit context, exclusions, or mention of alternative tools, leaving the intended usage partially 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?

The annotations already declare read-only and idempotent behavior, and the description adds significant context: no platform credentials are required, each query costs prepaid credit, the data is not confidential, and the client bears LGPD responsibility. This goes well beyond the annotation hints and helps the agent understand the tool's operational implications, though it stops short of detailing rate limits or exact return 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 compact at just three sentences, front-loading the core purpose before addressing cost, hosting, and legal aspects. Each sentence contributes distinct information without redundancy, though the LGPD sentence adds length and might be streamlined.

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 two-parameter read-only tool with no output schema, the description covers purpose, data source, hosting, cost, and data protection obligations. The lack of return-value documentation is a minor gap, but the provided context is otherwise sufficient to understand the tool's 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?

With zero schema description coverage, the description needed to compensate by explaining the cpf and cnpj parameters, but it does not. While the variable names are self-explanatory, the description neither details their formats nor clarifies how they relate (e.g., required fields, mutual exclusivity). Since both are optional in the schema, an agent has insufficient guidance to construct valid requests.

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 ('consulta') against the official CNDT source at Tribunal TST, providing a specific verb and resource. It distinguishes itself from generic platform siblings like authenticate or show_version by naming the exact domain and purpose. The scope is unambiguous: retrieving citizen-accessible data from official Brazilian sources.

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 through context—this is for querying CNDT data from official sources, with a pay-per-query model and no platform credentials. However, it does not explicitly state when to use or not use this tool, nor does it name alternatives among the sibling tools. The guidance is only implicit.

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