Tribunal TRT2: Consulta Processual

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

Annotations (idempotentHint true, destructiveHint false) already cover safety. The description adds context about permanent vs. session-only behavior and that calling with no args returns a link, but doesn't detail error handling or token validation. This adds value beyond annotations, though a bit more nuance could be given.

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 context, and every sentence adds actionable information. It efficiently packs both authentication modes and the exact call signature without verbosity.

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, the description covers all usage scenarios: permanent vs. session, how to obtain a link, and expected input format. It is complete enough for an agent to decide and invoke 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 has 0% coverage for the token parameter, but the description fully explains it: token accepts a JWT when pasted, and omitting it returns a link. This completely compensates for the missing schema documentation.

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 authenticates the MCP.AI server for IDE agents, with specific verb+resource and distinct methods (config header vs. token). It effectively differentiates from siblings like 'connect' by detailing the authentication flow.

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 describes when to use each approach: permanent via config header, session-only via token, and how to initiate (with or without token). It also instructs to call with no args to get a link, providing clear contextual 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 annotations already declare a read-only, idempotent, non-destructive operation, so the description does not need to repeat that. It adds meaningful behavioral detail by specifying `authenticated:true` and `empty pending[]` on success, and the presence of `connect_url`/URLs when credentials are missing.

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 concise sentences front-load the essential purpose and then give the two relevant state-dependent behaviors. There is no redundant wording, and every sentence contributes necessary context.

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 input schema, no output schema, and straightforward annotations, the description covers the key user-facing behaviors of connection status and URL availability. It leaves the exact handling of partial provider-connection states implicit, but the explanation is generally sufficient for an AI agent deciding to call this 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?

There are zero parameters, so the input schema cannot add more meaning. The description gracefully avoids parameter duplication and instead clarifies the results, which is enough for a parameterless tool.

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 begins with a specific verb and result resource: 'Returns connection status and URLs.' It also explains expected outcomes for connected vs. missing credentials, clearly distinguishing this as a status/inspection tool rather than the sibling `authenticate` 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 outlines when the tool returns useful output (all providers connected vs. missing credentials), but it does not explicitly state when to prefer this tool over alternatives like `authenticate`, nor does it give exclusions or prerequisites.

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 discloses far beyond the minimal annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false). It reveals the one-off execution semantics of invoke, the connect-link behavior for credential needs, checkout/top-up links for empty wallets, permission requirements (writes require workspace owner/admin), and installed_in_toolkit vs installed_in_workspace flags. This is the model of behavioral transparency — 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 dense (~250 words) and every sentence earns its place — no fluff or tautology. It front-loads purpose, then flows through the core discover→execute chain, key behaviors, permission notes, and the prompt library. The main weakness is structural: it's a single run-on paragraph where the marketplace core and prompt library could benefit from separation, and the KEY emphasis is capitalized mid-paragraph. Appropriate length given a 14-action, 23-param 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?

For a tool of this complexity (14 actions, 23 params, no output schema, minimal annotations), the description is remarkably complete: it covers the end-to-end flow, auth/financial fallbacks, permission scoping, the installed-vs-invoke distinction, and the entire prompt-library subsystem. With no output schema present, it compensates by describing return behaviors (connect link, checkout link). Minor gaps: not every action's return shape is described, and a few params lack semantic anchoring, but for this scope it is near-comprehensive.

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 and 23 parameters, the description carries the full burden — and it compensates well by walking through the action flows: action=search/describe/invoke/install, tool_id ('so you pick the right tool_id'), mcp_id, arguments, tier_slug, and the prompt library params (prompt_body, prompt_slug, prompt_description). However, some parameters remain unaddressed (limit, message, immediate, conversation, cancel_reason, cancel_comment, report_context, request_name, request_details, prompt_targets, prompt_vars), so it's not exhaustive despite strong coverage of the key decision-driving params.

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 specific, unambiguous statement: 'The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It then enumerates the core flow (search → describe → invoke) and the prompt-library subsystem, making the tool's dual role (catalog + executor) unmistakable. This clearly distinguishes it from siblings like tribunal_trt2_processo_consultar or 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?

Exceptionally explicit guidance: 'KEY: invoke works even when the MCP is NOT installed... prefer invoke for a single/occasional use' contrasted with 'Use install only to make an MCP PERMANENT in the active toolkit.' It also gives when-to-use for every action — list_tools ('lists what is callable right now'), request_mcp ('asks us to build a NEW MCP when nothing fits') — and disambiguates alternatives within the same family.

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 indicate idempotentHint=true, meaning repeated calls should have no additional effect, and the description doesn't contradict this. However, it doesn't disclose any side effects (e.g., where feedback goes, if it creates a ticket) beyond what annotations already imply. With readOnlyHint=false Annu, the description adds minimal behavioral 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 concise, one sentence, and front-loaded with the main purpose. The instruction to include the conversation array is important and earns its place. It's efficient without 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?

Given the tool has 3 params but only 1 required, no output schema, and zero schema coverage, the description covers the primary purpose and a key usage tip (including conversation). However, it doesn't explain what happens after reporting, what 'context' is for, or what makes a good 'message', so incomplete for a feedback 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%, so the description must compensate for parameter meaning. It explains 'message' implicitly (the feedback content) and 'conversation' (recent messages), but 'context' is not clarified. The description adds value for 'conversation' but leaves 'context' ambiguous, so a 3 is appropriate.

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 feedback. It distinguishes from siblings by focusing on reporting issues rather than other actions like authentication or marketplace operations, though it doesn't explicitly differentiate from a generic 'share feedback' tool.

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 users want to report issues, but it doesn't explicitly state when NOT to use it or mention alternatives. It mentions including the conversation array for reproduction, which provides some context for effective usage, but lacks 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, establishing a safe read operation. The description adds clarity that the version info specifically pertains to 'MCP platform and adapter', which is useful but goes little beyond the annotations. There is no contradiction; the description appropriately characterizes the 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 single, concise, front-loaded sentence with no wasted wording. Every word contributes to the meaning.

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 version query, the description is complete. Although there is no output schema and no mention of the return value, the simplicity of this tool makes the description fully adequate for an agent to invoke 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?

With zero parameters and 100% schema description coverage, there is nothing for the description to clarify. The baseline of 4 for the parameter-free case is appropriate here.

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 a clear resource ('the current MCP platform and adapter versions'). It is unambiguous and readily distinguishes itself from the sibling tools (authenticate, connect, etc.) which concern authentication, connections, and marketplace operations.

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?

No usage guidance is provided. The description does not specify when to use this tool vs. the sibling tools like 'toolkit_info' or 'marketplace'. While arguably not necessary for such a simple version tool, the rubric requires explicit guidance for a score above 2.

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 destructiveHint=false, and the description is consistent by describing a non-mutating informational operation. It adds valuable context by specifying exactly what state is reported (installed MCPs, connection status, accounts, catalog tool counts), going beyond the structured 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 that is front-loaded with the verb and immediately specifies the output content. Every detail earns its place, with no redundant or filler wording.

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 and has no parameters or output schema, but the description lists all the major categories of returned data (MCPs, statuses, accounts, catalog counts), making the expected result clear enough for most use cases. It does not describe the exact data structure, but for this simple tool the items are already sufficient.

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 zero parameters, so the baseline is 4. The description does not need to explain parameter semantics; it fully describes what info is returned by the no-argument call.

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', names the exact resource ('current toolkit state'), and enumerates what that state includes: installed MCPs, connection status, connected accounts, and catalog tool counts. This differentiates it from siblings like show_version and 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 clear context for when to use the tool: anytime you need the toolkit's current status overview. It does not explicitly name alternatives or exclusions, but for a zero-parameter status tool this is hardly necessary; the purpose makes usage inferable.

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 (read-only, idempotent), the description adds behavioral context: hosted by the platform, no credentials required, payment per consultation, and data privacy responsibilities under LGPD. This enriches the user's understanding of operational and compliance aspects.

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 somewhat redundant, repeating the idea of 'official source' and 'consultation' multiple times. It also includes legal disclaimers that lengthen the text without adding immediate operational value. While not overly long, it could be more 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?

The description covers the tool's purpose, source, payment, and data privacy, but omits any explanation of the input parameters or expected output. Given the absence of an output schema, return values are not required, but parameter clarification would enhance completeness.

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 two parameters ('grau' and 'numero_processo'), but the description provides no explanation of their meaning, format, or usage. With 0% parameter coverage in the description, it fails to compensate or clarify, leaving users to infer from parameter names alone.

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 legal processes at TRT2 (Tribunal Regional do Trabalho). It uses the verb 'consultar' and specifies the resource 'processo' and the court, distinguishing it from unrelated sibling 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 implies usage for consulting official Brazilian legal sources but does not explicitly state when to choose this tool over others. It mentions payment with prepaid credit, but lacks clear guidance on alternative tools or specific scenarios.

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