Dieta.ai

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

The description adds practical behavioral context beyond the annotations: it explains the browser login flow, token copying, permanent vs session-only login, and how to use the returned auth link. It does not fully describe the output/return behavior, but this is not required given the simple optional-token interface and idempotentHint annotation.

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 dense, and the admin side of the flow is clearly separated from the agent-facing call. The phrasing is slightly informal ('paste', 'log in in the browser'), but there is no significant 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 with one optional parameter and no output schema, and the description covers both the permanent and session-only authentication paths as well as the user interaction flow. It could be more precise about what the no-argument call returns, but the essential behavior 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?

The schema provides only a string parameter 'token' with no description. The tool description compensates well by explaining that token is a JWT, that it is optional, how to call with it ('{ token: "<jwt>" }'), and when to call without it. This adds meaningful parameter semantics 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 indicates the tool handles authentication/login: 'log in in the browser, copy the access token' and supports both permanent and session-based login. It does not explicitly distinguish itself from the sibling 'connect' tool, but its purpose is reasonably specific to authentication.

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 explicit guidance on when to use which option: adding the token to server config for permanent access, or pasting it for a session-only login. It also specifies calling with 'no args to get the link', directly instructing the agent on the correct invocation steps.

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 confirm read-only and idempotent behavior, and the description adds valuable output specifics: authenticated:true with empty pending[] when connected, and connect_url plus per-install URLs when missing credentials. This enriches understanding of return values without contradicting 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?

Two sentences, front-loaded with the tool's purpose, and no fluff. Each sentence adds critical behavioral details without 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?

With no output schema, the description fully explains the return structure for both possible scenarios. It covers all necessary information for an agent to use the tool correctly, including output fields and their meaning.

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?

No parameters exist in the schema, and schema coverage is trivially 100%. The description does not need to elaborate on parameters; baseline per rubric is 4.

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 returns connection status and URLs, with specific behavior for both connected and missing-credential states. It distinguishes itself from 'authenticate' by focusing purely on status 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 usage for checking connection status but does not explicitly contrast with alternatives like 'authenticate'. It provides no when-to-use or when-not-to-use guidance, leaving the agent to infer based on tool name and sibling context.

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 covered. The description adds only the 'user_diabetes block' context and no behavioral details such as return shape, data source, or authentication requirements.

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 short phrase with no redundancy, which is concise. However, it is under-specified as a noun fragment rather than a full sentence, so brevity comes at the cost of operational clarity.

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 read-only tool with no parameters and no output schema, the description gives the general topic but not what the agent will receive or how it relates to other diabetes-related tools. It is minimally recognizable but not fully 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 and schema description coverage is 100%, so there are no parameter semantics for the description to clarify. The baseline of 4 for a no-parameter tool 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 identifies the resource as 'Configurações/perfil de diabetes do usuário (bloco user_diabetes)', which makes the subject clear and distinguishes it from sibling tools by naming the user_diabetes block. However, it lacks an explicit verb or action, so it reads as a label rather than a statement of what the tool does.

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?

There is no guidance about when to use this tool versus related siblings such as dietaai_user or dietaai_goals. No alternatives, exclusions, or contextual triggers are mentioned.

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 (readOnlyHint: true, idempotentHint: true, destructiveHint: false) cover the safety profile, so the description doesn't need to repeat that. It adds the date format and states it's a summary, but doesn't describe what the summary contains, possible errors, or dependencies. Since annotations handle core safety, a 3 is appropriate—it provides some extra context but no deep behavioral detail.

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 front-loads the purpose and includes the parameter format. There is zero waste—every word adds value. It's appropriately sized for a simple 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?

The tool is simple with one parameter and no output schema, so the description needn't explain returns. It adequately states the purpose and parameter format. However, it doesn't clarify what the diary summary includes or how it differs from sibling tools like diaries, which might affect completeness for an agent deciding between tools. Given the low complexity, a 3 is fair.

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 declares 'date' as a string with no description or format. The description explicitly specifies the format YYYY-MM-DD, which is essential information beyond the schema. For a single parameter with 0% schema coverage, this fully compensates, making the parameter actionable.

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 a summary (Resumo) of a day's journal, with the date format specified. It identifies the resource (diary) and action (summary), making the purpose clear. However, it doesn't distinguish from sibling tools like dietaai_diary_logs or dietaai_diary_scheme, which could have similar 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?

No guidance is provided on when to use this tool versus alternatives. There are no exclusions, prerequisites, or context indicating which scenarios call for this tool. The description implies usage for a single day but gives no comparison with related diary tools.

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 clear. The description adds that it returns historical lines and how dates filter, but does not disclose additional behavior such as pagination, limits, or sorting. 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 single, well-structured sentence that front-loads the purpose, includes a concrete example, and mentions key parameters. No redundant words 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 simplicity of the tool (3 params, no output schema), the description adequately covers the purpose, required parameter, and filtering logic. It does not explicitly differentiate from siblings, but the tool name and description together provide enough context for a straightforward read 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?

Schema description coverage is 0%, so the description must compensate. It clarifies that diaryId is required and explains the conditional behavior of the two date parameters (both must be provided to filter). This adds meaningful semantic context beyond the bare schema, though it omits date format or inclusivity details.

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 it provides historical lines of a diary track (with glucose series as example), which is specific enough to distinguish it from generic diary or log tools like dietaai_diary and dietaai_log. However, it lacks an explicit verb such as 'list' or 'get', so the action is implied rather than stated.

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 specifies that diaryId is mandatory and that start_range_date and end_range_date filter the interval only when both are provided. This tells the agent when to supply parameters but does not explicitly mention when to use this tool versus alternatives like dietaai_log or dietaai_diary, nor any 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, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds domain nuance that it covers user-specific tracks (e.g., glucose), but it does not describe the return shape or behavior beyond that, which is acceptable but not exceptional.

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?

One short sentence, front-loaded with the core noun phrase, no wasteful words. It is concise and easily parsed.

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 no-param, read-only metadata tool, the description names the subject but leaves the output structure unspecified. Given no output schema and a vague noun-phrase description, an agent might not know whether it receives an array, object, or field list. More detail about the response would improve 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 tool has zero parameters, so the description cannot add parameter semantics; the baseline of 4 applies because no parameter reconciliation is 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 identifies the resource as definitions of user diary items (tracks), distinguishing it from sibling tools like dietaai_diary by focusing on the scheme rather than the data. However, it lacks an explicit verb such as 'returns' or 'lists', so the action is implied rather than stated.

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 guidance is given about when to use this tool instead of siblings like dietaai_diary or dietaai_diary_logs. The context of a static definition/scheme is implied, but the description does not state 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the tool's safety profile is disclosed. The description adds behavioral context beyond annotations by specifying the return structure (items per meal and daily totals with macro breakdown), which helps the agent anticipate the response format 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 a single, well-structured paragraph that front-loads the purpose, follows with usage rules, and ends with the return format. Every clause adds value, with no filler or repetition. It is concise yet comprehensive.

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 low complexity (3 optional params, no nested objects, no output schema), the description covers all necessary aspects: what it does, how to invoke it correctly (single day vs. range with max limit), and what the response contains. Combined with the informative annotations, this is fully complete for an agent to use 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?

Schema coverage is 0%, so the description fully compensates by explaining the semantic purpose of each parameter: 'date' is for a single day, while 'start_date'/'end_date' define a range, and the max 31 days constraint adds meaning that the schema's plain string types lack. This is essential for correct invocation and goes well beyond the 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 starts with 'Consulta o log de alimentos do usuário com os macros', clearly stating the action (query) and the resource (user's food log with macros). It distinguishes this from sibling tools by focusing on food log and macro details, and the later phrase 'Retorna os itens por refeição e os totais do dia' clarifies the output scope. This 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 states when to use 'date' for a single day versus 'start_date/end_date' for a range, and includes a maximum range of 31 days. This gives clear conditional guidance for parameter selection, which is more detailed than typical descriptions and directly addresses usage 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 readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by noting that goals include custom goals and that this data is meant to be compared against food totals. It does not describe return format or edge cases, but for a simple read-only tool this is adequate.

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 well-structured: the first sentence defines the resource and its contents, and the second sentence provides actionable usage guidance. There is 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?

Given that the tool takes no parameters, has no output schema, and is marked as a safe read, the description is nearly complete. It tells the agent what data is available and suggests how to use it with dietaai_foods. It could be slightly more explicit about output granularity or date handling, but the low complexity and strong annotations make this 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 tool has zero parameters, so there is no parameter documentation burden. Per the rubric, the baseline for zero-parameter tools is 4. The description does not need to compensate for undocumented schema fields, and it does not.

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's resource—the user's nutritional goals—and enumerates specific goal types (calories, protein, carbs, fat, water, fiber, custom). It distinguishes itself from siblings by suggesting comparison with dietaai_foods. However, it lacks an explicit action verb such as 'retrieves' or 'lists', so it stops short of a fully explicit purpose statement.

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 concrete guidance: compare this tool's goal data with dietaai_foods totals to assess adherence. This frames the primary use case and differentiates it from sibling tools. It does not explicitly state when not to use the tool, but the context is clear enough for basic selection.

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, covering the safety profile. The description adds the fact that OAuth binding is done once in the app, which is useful context. However, it does not disclose other behaviors such as rate limits, error handling, or typical response characteristics beyond saying the path follows the provider's docs. With annotations present, this is adequate but not rich.

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—two sentences—yet packed with essential information: the tool's purpose, the provider options, and example paths. It is front-loaded with the core purpose and immediately provides actionable examples. There is no filler or 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?

For a simple proxy with only two parameters and no output schema, the description covers the input semantics well but leaves the return format implicit ('the path follows the provider's docs'). It does not describe error cases, pagination, or how the response relates to the provider's API. Given the tool's simplicity, it is adequate but could benefit from a note that the response mirrors the provider's API format.

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 provides only parameter names and an enum for provider, with zero description coverage. The tool description compensates by explaining the 'path' parameter with concrete examples for each provider (e.g., Whoop 'v1/cycle', Strava 'api/v3/athlete'), which adds significant meaning. The 'provider' parameter is self-explanatory via its enum. Although not exhaustive, it provides enough semantic context for correct 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 it is an HTTP bridge to the official APIs of wearables (Whoop, Strava, Oura) linked to the account. It provides specific example paths, which clarifies its role as a proxy. While it doesn't use a standard verb like 'get' or 'fetch', the intent is clear and distinct from the more specialized sibling tools like dietaai_libre_data or dietaai_sibionics_data.

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 suggests usage when needing data from these wearable providers, but it does not provide explicit guidance on when to choose this tool over the specialized siblings. It mentions that OAuth is already bound, which is a prerequisite, but lacks direct 'use when' or 'prefer if' statements. The context is understandable but not explicitly framed as a decision guide.

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, destructiveHint=false. The description adds a hint about the body being optional and matching what the app sends after connecting, which provides some context about input, but it doesn't describe return format, errors, or other behavioral traits. Since annotations cover safety, a score of 3 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 extremely concise (two sentences), directly states the purpose, and front-loads the key information. 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?

Given the tool returns glucose data, the description omits critical details like whether authentication is required, how to obtain data (e.g., filters, time ranges), and what the output looks like. There is no output schema, so the description must explain more, but it only gives a cryptic hint about the body. The description is inadequate for a tool that likely serves as a data integration point.

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 one optional 'body' object with arbitrary properties and 0% coverage in the description. The description adds meaning by indicating the body should match what the app sends after connecting, which helps the agent understand the expected structure. However, this is vague and doesn't provide concrete details on the body's contents, so it partially compensates for the schema's openness.

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 states it returns glucose data from LibreView (Libre), which is a specific resource. It distinguishes from siblings like dietai_sibionics_data by naming Libre, but it doesn't specify what kind of glucose data (historical, real-time) or the exact scope.

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 after connecting ('após conectar') but gives no explicit guidance on when to use this tool vs alternatives, no prerequisites like authentication, and no exclusions. The connection is implied but not stated clearly.

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, the description discloses that this is fire-and-forget, returns acceptance, and processes asynchronously. This adds meaningful behavioral context that the annotations do not provide. No contradiction with annotations was found.

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: two sentences cover purpose, input method, required combination, and asynchronous behavior. Every sentence adds value and there is no unnecessary 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 3-parameter tool with no output schema, the description covers the main purpose, required fields, and async return behavior. However, the 'context' parameter is unexplained, and there is no explicit guidance on when to prefer this over sibling logging tools, leaving the context incomplete.

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 gives semantic meaning to 'prompt' and 'imageUrl' by explaining they carry text and photo URL respectively, and it clarifies the required combination. However, the 'context' parameter is completely undocumented, and with 0% schema description coverage this is a clear 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?

The description uses a specific verb ('Registra') with a clear resource ('uma refeição') and explains the input mechanism (texto e/ou URL de foto). It also distinguishes itself from siblings by referencing the same AI pipeline as the app's chat, which is concrete and useful.

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 logging meals with text/image and states the required combination ('Pelo menos um de prompt ou imageUrl é obrigatório'). However, it does not explicitly compare this tool with siblings like dietaai_diary_logs, so the when-to-use guidance is only 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?

Annotations already declare the tool as read-only, idempotent, and non-destructive, so the bar for transparency is lower. However, the description adds minimal insight about behavior beyond that, only vaguely referencing an optional body and passthrough without explaining effects or limitations.

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 very brief, which is concise but lacks substance. It avoids verbosity, but the brevity results in under-informativeness. It is not as well-structured as a clear two-sentence summary, so it does not fully achieve a high conciseness score.

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 that there is no output schema, the description should explain what the tool returns. It only mentions 'gráfico' (chart) without detailing the response format, data fields, or any side effects. The description is insufficient for a developer to understand the tool's full 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?

The schema shows a single 'body' object but provides no details about its structure or purpose. The description does not compensate, only repeating 'Body opcional' and 'passthrough' without elaborating on what fields are expected or how they influence the response.

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 indicates the tool provides glucose data from Sibionics in chart form, but it lacks specificity about the exact operation (e.g., retrieval, query) and does not differentiate it from other data-related tools. It is somewhat vague, so it earns a mid-range score.

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 only mentions an optional body and passthrough, which are not usage instructions. There is no mention of prerequisites, frequency, or context.

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 read-only, idempotent operation. The description adds the key detail that inviteId is sourced from the user's profile rather than the client, which is useful. However, it does not describe the response format or any potential limitations.

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 short sentences, front-loaded with the core purpose. No wasted words, and the critical clarifying note about inviteId is placed second.

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 read-only fetch with no parameters and no output schema, the description covers the essential purpose and a key behavioral nuance. It could mention what the glucose data represents (e.g., current reading, historical trend), but it is adequate as-is.

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 high. The description clarifies an implicit parameter (inviteId) that might otherwise be confusing to agents, explaining its origin without requiring 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 retrieves glucose data from the Sibionics device. It distinguishes from generic 'dietaai_sibionics_data' but does not explicitly call out that it is narrowly scoped to glucose readings.

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 implies the tool is used when glucose data from Sibionics is needed, but does not explicitly mention when to prefer it over sibling tools like 'dietaai_sibionics_data' or 'dietaai_libre_data'. The note about inviteId provides internal context but not usage 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?

The description adds no behavioral context beyond what the annotations already declare (readOnlyHint=true, idempotentHint=true, destructiveHint=false). It does not disclose any additional constraints like authentication requirements or return format nuances, so it fails to enrich the annotation-provided information.

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 redundant words. It is front-loaded and to the point, efficiently conveying the core subject.

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?

While the tool is simple, the description is too vague: it says 'subscription data' without specifying what fields or type of information is returned (e.g., plan, status, expiry). With no output schema and no parameter information, the description carries the full burden and fails to provide sufficient detail for the agent to know what to expect.

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 schema coverage is trivially 100%. Per the baseline rule for zero-parameter tools, the description need not explain parameters, and it correctly avoids adding irrelevant detail.

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 resource (subscription data) and implies a read operation, distinguishing it from sibling tools like dietaai_user or dietaai_goals. However, it lacks an explicit verb like 'retrieves' or 'lists', making it slightly less action-oriented, but the purpose is 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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention any specific context, prerequisites, or exclusions, leaving the agent to infer usage from the name alone.

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 clear. The description adds useful context about the returned fields but does not disclose behavior such as whether this is the current authenticated user's profile or whether authorization is required.

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 front-loads the core purpose and lists the key fields. Every word earns its place, with no filler 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?

Given the tool has no parameters and no output schema, the description adequately communicates the return contents by listing the profile fields. It could be slightly more complete by clarifying that it refers to the current authenticated user, but for a simple read-only profile tool it is largely 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 tool has zero parameters, so there is no parameter semantics burden on the description. The baseline of 4 applies because no parameter documentation is 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 identifies the tool as exposing the basic user profile ('Perfil básico do usuário') and enumerates its fields: nome, email, userId, fuso horário, idioma e data de criação. It is specific enough to distinguish from sibling tools like dietaai_subscription or dietaai_diabetes, though it lacks an explicit verb such as 'retrieves' or 'gets'.

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, nor does it mention prerequisites like authentication. It is implied that this is the profile-access tool, but there is no explicit context or exclusion of sibling tools.

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 only carry readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false. The description adds substantial non-obvious behavior: invoke runs one-off without installing and 'without bloating the tool list', returns a connect link when credentials are needed, returns a checkout/top-up link when the wallet is empty then 'you retry', writes require owner/admin, and search/describe flag installed status. It is consistent with openWorldHint=true and readOnlyHint=false — no 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?

At roughly 280 words, this is long, but the tool's complexity (14 actions across two domains) justifies it. The structure is front-loaded: core flow first, then KEY invoke behavior, then install-vs-invoke contrast, permissions, and the prompt library last. A few phrases are slightly redundant (e.g., restating the permission requirement after already covering invoke), but each major sentence 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?

Given 23 parameters, zero schema coverage, no output schema, sparse annotations, and 14 actions, the description covers the flow, failure modes, permissions, and the second domain impressively. Gaps remain: the 'resume' action in the enum is never mentioned, expected return shapes for actions like invoke are not described, and the limit parameter's pagination purpose is unclear. For a tool this complex, it's thorough but not exhaustive.

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 must compensate, and it does well: the action-driven flow explains search → describe → invoke and ties action enum values to behaviors (tool_id from describe profile, mcp_id, prompt_slug/prompt_vars/prompt_body for the prompt library). However, several of the 23 parameters remain unexplained — limit, message, conversation, immediate, tier_slug, cancel_reason/cancel_comment, prompt_targets, prompt_title, prompt_description, prompt_category, report_context — leaving gaps the agent must infer.

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 states the exact purpose: 'the official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them.' It provides a specific verb+resource for each action (search discovers MCPs, describe returns a profile, invoke runs a tool) and clearly distinguishes the marketplace's dual role (catalog + runner) plus the prompt library sub-domain.

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 explicit when-to-use guidance: 'prefer invoke for a single/occasional use', 'Use install only to make an MCP PERMANENT', and 'request_mcp asks us to build a NEW MCP when nothing fits.' It also names alternatives like list_tools ('lists what is callable right now') and search_prompts/get_prompt for the prompt library branch, making routing unambiguous.

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=false, destructiveHint=false, and idempotentHint=true. The description adds the behavioral instruction to include the conversation array for reproduction but does not explain side effects, whether a ticket is created, or any asynchronous behavior. Given the annotations cover the safety profile, this is adequate but not rich in behavioral disclosure.

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 no fluff. It front-loads the purpose and provides a concise usage instruction. 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 simple feedback tool with 3 parameters and no output schema, the description is reasonably complete. It states what it does and gives a key usage hint. However, it could elaborate on the format of the conversation string and clarify that 'context' is optional, but given the tool's simplicity and existing annotations, 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?

The schema's description coverage is 0%, so the description must compensate. It mentions the 'conversation array' but does not explain that the 'conversation' parameter is a JSON array string (default '[]'), nor does it explain the 'context' parameter. It only hints at the required 'message' parameter implicitly. This leaves significant ambiguity about parameter formats and purposes.

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 uses a specific verb ('report') and resource (bug/feature/feedback), and it distinguishes itself from sibling tools since none others handle reporting or feedback. The instruction to include the conversation array adds specificity for reproduction.

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 states when to use the tool (for bugs, missing features, or feedback). It also provides a usage hint: 'Include the conversation array with recent messages for reproduction.' It does not explicitly say when not to use it or mention alternatives, but among siblings there is no obvious alternative, so the guidance is clear enough.

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, which cover safety. The description adds specificity about which versions (MCP platform and adapter), providing useful context beyond 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, front-loaded sentence with no fluff. Every word contributes to the purpose, making it optimally 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?

For a zero-parameter, no-output-schema tool, the description is fully complete. It names exactly what will be shown (MCP platform and adapter versions), which is all the agent needs to invoke or understand 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?

The tool has zero parameters, and per the rubric baseline 4 is appropriate since the schema already fully covers the empty parameter set. The description adds no parameter-related detail because none 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 'Show the current MCP platform and adapter versions' uses a specific verb (show) and resource (versions), clearly distinguishing it from sibling tools that focus on data operations. It is unambiguous and directly states what the tool does.

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 for checking versions but provides no explicit when-to-use or contrast with alternatives. While sibling tools like toolkit_info might overlap, no guidance is given, so it remains at baseline 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds valuable context beyond annotations by specifying what the returned state includes (MCPs, connection status, accounts, catalog tool counts), giving the agent a concrete picture of the operation's result.

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 lists all relevant output categories without repetition or filler. Every word adds value.

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 info tool with no output schema, the description fully covers what the agent needs to know: what the tool returns and the scope of the state. No additional behavioral or return-format details are necessary for this level of complexity.

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 need not compensate for schema gaps. The baseline of 4 applies because there are no parameter semantics to clarify; the description correctly focuses on the return value.

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 a clear resource ('current toolkit state'), then enumerates the exact contents: installed MCPs, connection status, connected accounts, and catalog tool counts. This clearly distinguishes it from the sibling tools, which are domain-specific or auth-related.

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 an agent needs to inspect toolkit state, but it does not explicitly state when to use it versus alternatives like connect or authenticate. There are no exclusions or alternative tool mentions, so 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.