Tribunal TRF3: Obter Certidão de Distribuição

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

Annotations already indicate idempotent and non-destructive, and the description adds context about behavioral traits: it can establish a permanent connection via config header (non-expiring) or a session-only login via pasted token, plus the no-args behavior returns a link. This complements the annotations 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 a single, somewhat dense paragraph but each clause provides critical information: target audience, login flow, two token-handling options, and the no-arg call. It could be split into bullet points, but the current structure is efficient and 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 has one optional parameter and no output schema, the description covers all necessary details: how to authenticate, where to place the token, session vs. permanent options, and the no-arg link retrieval. It is fully sufficient for an agent to use 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 a single 'token' string with no description and 0% coverage, but the description explains that 'token' is a JWT pasted after user authentication, and explicitly notes that calling with no args yields the link. This adds essential meaning beyond the bare schema.

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 user for IDE agents (e.g., Cursor), with specific actions: log in via browser, copy access token, and either set a header in config or provide a token for session. This is a specific verb+resource with scope and differentiates from sibling tools like '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?

It explicitly explains when to use the tool (for IDE agents), provides two usage modes (permanent config header vs. session-only token), and instructs to call with no args to get a link. It doesn't explicitly state exclusions or alternatives, but the guidance is clear enough for an agent to decide 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?

Annotations already indicate a safe read-only, idempotent operation. The description adds valuable behavioral detail about conditional responses: when authenticated returns 'true' with empty pending[], when credentials are missing returns connect_url and per-install URLs. This goes beyond what annotations state.

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 exactly two sentences: the first gives the main purpose, the second explains conditional outcomes. It is front-loaded, free of fluff, and every word contributes to understanding the 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 parameterless, read-only status tool, the description fully covers expected behavior in both normal and missing-credential cases. It mentions the key fields (authenticated, pending[], connect_url, per-install URLs) and no output schema exists, so this is sufficiently 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 schema is fully covered by default. The description correctly avoids inventing parameter details; the baseline for 0 params is 4 because there is nothing to explain beyond what the schema shows.

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 a specific verb ('Returns') and resource ('connection status and URLs'). It distinguishes itself from siblings like 'authenticate' by focusing on status rather than action, and the conditional outcomes add precision.

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 about when to use it: to check connection status and retrieve URLs. It also explains the two states (all connected vs missing credentials) and what the output will be, which guides usage. It doesn't explicitly mention alternatives or exclusions, but the context is sufficient.

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?

Massive behavioral disclosure well beyond the annotations: invoke's one-off execution semantics, when connect vs. checkout links are returned, that credential/payment flows require a user retry, that writes require workspace owner/admin, and that published prompt links 'open without login.' No contradiction with readOnlyHint=false or openWorldHint=true.

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?

Every sentence earns its place, but the whole thing is one enormous, dense paragraph. The core flow would be far more scannable with sentence breaks around logical clusters (discovery, execution, auth, billing, prompts). It is information-dense but not structured for consumption.

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 this complex (15 actions, 23 optional params, no output schema), the description covers an extraordinary amount: core flow, edge cases (empty wallet, missing wallet, login), auth requirements, and the prompt library. It stumbles only on some parameter semantics and return-value shape, which the lack of an output schema makes more noticeable.

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 masterfully explains the `action` parameter's enum values and the core data flow (mcp_id, tool_id), and alludes to prompt_vars via `{{variables}}`. However, with 0% schema coverage and 23 parameters, several fields — `immediate`, `tier_slug`, `conversation`, `cancel_reason` — receive zero explanation. The description covers the 80% case well but falls short of full 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 opens with a crystal-clear definition ('The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them') and reinforces it with concrete example intents. It provides a specific flow (search → describe → invoke) that fully distinguishes this orchestrator from its siblings, which are narrow singletons like report_bug or show_version.

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 disambiguates when to use each mode: 'Use install only to make an MCP PERMANENT... prefer invoke for a single/occasional use' and contrasts the prompt library with MCP search. It even maps user-facing intent patterns ('is there a tool for Y') onto the correct action.

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 that the tool is for reporting and suggests including conversation context for reproduction, which is behavioral. The annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true) are consistent with a non-destructive reporting action, but the description doesn't add much beyond the annotations. It doesn't detail what happens after submission or whether feedback is logged, but that's not critical.

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, concise and front-loaded: it states the purpose first, then an actionable instruction. Every sentence adds useful information with 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 feedback/report tool with no output schema, the description explains the high-level inputs and purpose. It does not explain the 'context' parameter or how the conversation string should be formatted, which could be a gap. However, the tool is simple and the description covers the essential use case, so it is adequate.

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 the three parameters. It references 'conversation array' but the schema defines 'conversation' as a string (default '[]'), which may be a JSON string representing an array. The 'message' and 'context' parameters are not explained, though their meanings are inferable. The description adds some value by specifying the conversation parameter's role but leaves ambiguity about its format.

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 identifies the resource (bugs/feedback) and the action (report/send), which is sufficient for understanding the tool. However, it doesn't explicitly distinguish it from sibling tools, though the sibling list includes unrelated tools, so ambiguity is low.

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 guidance on when to use the tool by mentioning it covers bugs, missing features, and feedback. It also instructs to 'Include the conversation array with recent messages for reproduction,' which is a usage prerequisite. It lacks explicit alternatives or exclusions, but the context is clear enough for typical use.

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 and idempotentHint as true, and destructiveHint as false. The description adds the detail of what information is shown (platform and adapter versions), which is useful context beyond the annotations, but it does not disclose any additional behavioral traits or side effects.

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 that directly states the tool's function without any unnecessary words. It is perfectly structured and 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?

For a zero-parameter, read-only tool with no output schema, the description adequately conveys the purpose and scope. There are no gaps in the information needed by an agent to decide it can use this 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?

There are zero parameters in the input schema, so the baseline is 4. The description does not need to explain parameters, and the schema coverage is trivially 100% because there are none.

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 'shows the current MCP platform and adapter versions', using a specific verb and resource. It is distinct from sibling tools like authenticate, connect, and marketplace, 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 gives no guidance on when to use this tool versus alternatives. It only states the function, without any explicit context for invocation or comparisons to similar tools like toolkit_info.

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, indicating a safe read operation. The description adds valuable behavioral context by detailing the exact contents of the returned state (MCPs, statuses, accounts, counts), going beyond the generic 'read' label. It doesn't cover edge cases like rate limits or auth, but those are less critical for an annotation-backed read-only tool.

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 immediately states the purpose and uses a colon-enumerated list to convey the output fields efficiently. Every word adds value, with no fluff or repetition.

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 introspection tool, the description is complete: it fully explains what the tool returns (installed MCPs, connection status, accounts, catalog counts). No output schema exists, but the description covers the return shape sufficiently, and given the strong annotations, no additional context is essential.

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 empty schema fully covers parameter semantics. Per rubric guidelines, 0-parameter tools receive a baseline score of 4 because no compensation is required; the description doesn't need to explain parameters that don't exist.

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 the clear verb 'Returns' with a specific resource, 'the current toolkit state,' and enumerates exactly what that includes (installed MCPs, connection status, connected accounts, catalog tool counts). This level of specificity makes the tool's purpose unmistakable and distinguishes it from siblings like show_version.

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 the tool (to inspect toolkit state) but provides no explicit guidance on alternatives, exclusions, or when not to use it. It leaves the use case to inference rather than directly stating 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?

Adds details about being a paid query, using official data, not confidential, and data controller responsibilities, which significantly supplements the read-only and idempotent 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 concise and structured, but includes some repetition (e.g., 'consulta', 'informação'). It remains clear and not overly verbose.

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?

Covers key operational aspects such as payment model, data source, and LGPD compliance, but does not describe the output format or error scenarios. Given the simple input schema, this is fairly 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 description does not explain the individual parameters cpf, cnpj, and numero_certidao; however, their purpose is inferable from the tool's function and standard Brazilian identifiers, though ambiguity remains.

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 clearly states the tool obtains a Distribution Certificate from TRF3 via an official source, distinguishing it from sibling tools focused on authentication, connection, marketplace, and other functionalities.

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 mentions prepaid credits and no credentials needed, indicating prerequisites, but does not explicitly contrast with alternative tools. However, given the specific nature and limited sibling set, this is adequate guidance.

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