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

Annotations already indicate a write operation (readOnlyHint=false). The description adds that it creates a persistent custom food and returns a food_id, which is useful beyond annotations. It does not detail deletion or modification implications, but overall transparent.

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 front-loaded with the purpose and important usage note. The parameter list is structured but somewhat lengthy. Nonetheless, every sentence adds value, particularly given the need to compensate for schema deficiencies.

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 all 11 parameters, mentions return value usage, and provides a critical serving size caveat. With an output schema present, missing details like error handling are minor. Mostly complete for a creation 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?

With 0% schema coverage, the description fully compensates by detailing each parameter, including units (kcal, g, mg) and defaults (e.g., serving_name default '1 serving'). This adds essential meaning beyond the schema's bare property names.

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 explicitly states it creates a custom food with specified nutrition, distinguishing itself from sibling tools like search_foods and add_food_entry by noting the returned food_id is used with add_food_entry. The verb 'Create' and resource 'custom food' are clear.

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: use this to create a custom food, and then use add_food_entry to log it. It also notes that nutrient amounts are for the full serving size. However, it does not explicitly state when not to use or list alternative methods for similar tasks.

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

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

Annotations already indicate readOnlyHint=false (mutation) and openWorldHint=true. The description adds behavioral context by explaining parameter sources (food_id from search_foods, etc.) and effect (adds entry to diary), going beyond 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 well-structured with an Args section and clear sentences. It could be slightly more concise (e.g., default values could be omitted from text since schema shows them), but it remains effective.

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 6 parameters, 3 required, and an output schema (present but not shown), the description covers all parameters, workflow, and usage context comprehensively. It is complete for a mutation tool with these complexities.

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, the description fully compensates by explaining the meaning and source of each parameter (e.g., food_id from search_foods, date format YYYY-MM-DD, diary_group enum values). This adds significant value 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 clearly states 'Add a food entry to the Cronometer diary', uses a specific verb (add) and resource (food entry), and distinguishes from sibling tools like search_foods (for finding) and get_food_details (for confirming).

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 instructs to use search_foods and get_food_details before calling this tool, providing clear context. It lacks explicit when-not-to-use statements, but the workflow guidance 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?

The description explicitly states 'Additive -- does not remove existing entries on the destination date,' which adds meaningful behavioral context beyond the annotations. The annotations already indicate non-destructive behavior, but the description clarifies the exact merge semantics and what side effects will not occur.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the primary purpose, followed by the key additive behavior and a concise parameter explanation. Every sentence conveys essential information without repetition or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with one optional parameter, a clear source/destination model, an additive safety note, and an output schema, the description is complete. An agent has enough information to invoke the tool correctly without additional missing context.

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?

Although schema description coverage is 0%, the description fully documents the only parameter: date as YYYY-MM-DD with a default of today. It also clarifies that date is the destination date, which adds meaning beyond the raw schema definition.

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 verb 'Copy' and the resource 'all diary entries from the previous day' to a specified destination date. This is a specific, unambiguous operation that distinguishes copy_day from the provided siblings, none of which describe copying diary entries.

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 intended use is implied by the core statement 'Copy all diary entries from the previous day to the given date.' However, the description does not explicitly discuss when to prefer this tool over alternatives or mention any exclusions, so usage guidance is only implicit rather than clearly framed.

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, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral context by explaining that 'only tracked nutrients' appear and that to surface specific nutrients, targets must be set in Cronometer. It also describes the response structure and null values, which is valuable beyond 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 (under 100 words) and well-structured: first sentence states purpose, then breaks down the response, then notes on tracked nutrients, then parameter. No extraneous information, every 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 the moderate complexity and presence of an output schema, the description sufficiently explains what the tool returns and the condition for nutrient appearance. It could mention that data is user-specific, but this is implied by the context of the tool suite. Overall, it provides enough context for an agent to decide on usage.

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% description coverage, so the description carries the full burden. It provides format ('YYYY-MM-DD') and default behavior ('defaults to today'), adding meaning beyond the schema's bare property definition.

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 'Get daily nutrition summary with consumed macro and micronutrient totals,' specifying the verb ('get') and resource ('daily nutrition summary'). It distinguishes from sibling tools by describing the aggregate nature of the output, such as 'summary: flat macro totals' and 'nutrients: full list of tracked nutrients,' which is distinct from tools like get_food_log or get_nutrition_scores.

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 obtaining daily nutrition totals but lacks explicit guidance on when to use versus alternatives. It does not state when not to use or provide comparisons with sibling tools, leaving the agent to infer based on the output described.

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, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds valuable behavior beyond annotations: returned fields (status, timestamps, duration) and default date behavior (30 days ago to today), which helps the agent understand what a call will produce.

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: the core purpose appears first, then return contents, then parameter details. Every sentence contributes useful information without 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?

With only two optional parameters, rich annotations, and an output schema present, the description covers the essential call context: what is fetched, the default range, and the date format. Minor details like timezone handling or status enumeration are not specified, but these are not critical given the output schema and the simple read-only nature of 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 input schema only shows nullable string parameters with null defaults and 0% description coverage. The description compensates by documenting start_date and end_date as YYYY-MM-DD with explicit defaults, giving the agent the format and semantics needed to invoke the tool correctly.

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 verb and resource: 'Get fasting history from Cronometer.' It then clarifies exactly what is returned—'fasts within the date range including status, timestamps, and duration'—which distinguishes it from related siblings like get_fasting_stats, add_fast, or delete_fast.

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 a fasting history within a date range is needed, and the date-range context is clear. However, it does not explicitly mention alternatives or when not to use this tool, leaving the agent to infer the boundary against siblings like get_fasting_stats.

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 operation as read-only, idempotent, and non-destructive, so the description does not need to restate safety. The description adds meaningful behavioral context by specifying that the tool returns computed aggregates rather than raw entry data, which is not captured by the 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 extremely concise: one clear purpose sentence followed by a line enumerating return values. It is front-loaded with the core action and adds no filler or redundant information.

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 aggregated stats tool with rich annotations and an output schema, the description covers the essential information. It clearly states what the tool does and what it returns, and there are no hidden inputs or side effects an agent would need to know about.

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 is no parameter semantics burden on the description. The baseline of 4 applies because with no parameters, nothing additional 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 states that the tool retrieves 'aggregate fasting statistics' and enumerates the exact computed metrics returned: total fasting hours, longest fast, average fast duration, and completed fast count. This distinguishes it from the sibling get_fasting_history, which implies raw history rather than summary metrics.

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 word 'aggregate' and the list of summary metrics imply this tool is for overview statistics rather than detailed history, but the description does not explicitly mention when to prefer it over get_fasting_history or other fasting-related tools. No alternatives or exclusion criteria are named.

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 signal readOnlyHint, idempotentHint, and non-destructive behavior, so the description carries a lighter burden. It adds context about the full nutrient profile and measure_ids, but it does not describe further behavioral traits such as error cases or data availability. 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 compact, front-loaded with the primary purpose, and every sentence earns its place. The Args section adds provenance for the only parameter without excessive 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 single parameter, rich annotations, and presence of an output schema, the description is sufficiently complete. It also explains the important workflow relationship with search_foods and add_food_entry, which would otherwise be ambiguous.

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 food_id. It does so by specifying 'Food ID from search_foods results,' which gives the agent the essential source and provenance of the parameter beyond the bare integer type.

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 a specific verb and resource: 'Get detailed food information including nutrition and serving sizes.' It adds the key deliverable, 'full nutrient profile and available measure_ids,' and implicitly distinguishes itself from search_foods by being the follow-up detail lookup.

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 gives explicit usage context: 'Use this after search_foods' and explains that the output is 'needed for add_food_entry.' This clearly positions the tool in a workflow, though it does not explicitly list when-not-to-use 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?

The description significantly expands on annotations, detailing the `energy_summary` and `nutrition_summary` fields, including specific field meanings and usage instructions (e.g., 'Always report this when summarizing'). No contradictions with annotations (readOnlyHint, idempotentHint).

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 well-structured and concise, front-loading the main purpose and using bullet points for details. Every sentence adds value 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?

The tool has a single optional parameter and an output schema; the description thoroughly explains the return fields (`energy_summary`, `nutrition_summary`) and offers usage guidance, leaving no significant gaps 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 0% schema coverage, the description compensates by specifying the date parameter format (`YYYY-MM-DD`) and default behavior (`defaults to today`), adding meaningful context beyond the schema's minimal definition.

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 all diary entries for a given date, specifying the data returned including food names, amounts, meal groups, and nutrient data. It distinguishes itself from siblings like `get_daily_nutrition` and `get_food_details` by focusing on daily entries.

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 viewing daily logs, with explicit guidance on preferring `remaining_kcal` over manual derivation. However, it does not explicitly exclude scenarios or compare to alternatives like `add_food_entry` or `search_foods`.

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 fully covered. The description adds useful context by explaining that the result includes the per-day template mapping and all saved templates with their values, which clarifies what 'macro targets' means.

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 filler. The main purpose is front-loaded and the second sentence expands the meaning of 'weekly schedule and templates' 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?

Given the zero-parameter schema, rich read-only annotations, and presence of an output schema, the description provides all essential context. It clearly states what the tool returns, making it complete enough for an agent to select 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?

There are zero parameters, so the input schema fully documents the calling contract. The description correctly implies no arguments are needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Get'), a clear resource ('current macro targets'), and the precise scope ('weekly schedule and templates'). It also distinguishes the tool from the ambiguous sibling 'get_targets' by naming the macro-specific contents returned.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly indicates this is the tool to call when the agent needs the current weekly macro schedule or saved macro target templates. It does not explicitly name alternatives or exclusions, but with zero parameters and such specific return content, the usage context is obvious.

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. The description adds value by detailing the return structure: category scores, consumed amounts, confidence levels, and default date behavior. No contradictions.

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 is informative. The first sentence captures the core purpose. The second elaborates on return structure. The third provides usage guidance. There is no fluff or repetition, and it is well-structured.

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 explains the return content (categories, amounts, confidence) and date parameter behavior. With an output schema present, it does not need to detail every field. It is complete for understanding what the tool does and its main input. Minor gap: no mention of error conditions or timezone, but acceptable.

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 only parameter 'date' is described with format YYYY-MM-DD and default behavior (defaults to today). This adds meaning beyond the input schema, which only specifies string|null with no format or default. Schema coverage is 0%, so the description compensates well.

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 nutrition scores with per-nutrient consumed amounts and category grades. It explicitly distinguishes itself from siblings by calling it 'the richest nutrition endpoint' and contrasting with simpler endpoints like get_daily_nutrition.

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?

Provides explicit guidance on when to use: 'when you need to know both how much of each nutrient was consumed AND how close each is to the target.' Does not explicitly mention when not to use or name alternatives, but the context is clear.

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

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

The description states the core mutation (marking a day complete or incomplete), which aligns with the annotations readOnlyHint=false and destructiveHint=false. It does not add richer behavioral context such as side effects on streaks or handling of non-existent days, but the annotations already provide idempotency and destructiveness safety, lowering the burden on the description.

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 crisp sentences followed by a structured Args block. The purpose is front-loaded, and there is no filler or redundant explanation. Every 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?

For a simple two-parameter mutation, the description covers the operation and all parameter meanings. The output schema and annotations cover return values and safety characteristics. It stops short of a 5 by leaving edge-case behavior (e.g., what happens if the day does not exist) unmentioned, but this is a minor gap.

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 types and a default, with zero parameter descriptions. The description's Args block compensates fully by specifying the exact date format 'YYYY-MM-DD' and the boolean mapping 'True to mark complete, False for incomplete'. This gives an agent everything needed to set each parameter correctly.

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 a specific verb ('mark') and resource ('diary day') with the two possible states 'complete or incomplete'. It is clearly distinguishable from all sibling tools, which deal with foods, biometrics, exercises, and fasting rather than diary completion status.

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 for when to use the tool: whenever a diary day's completion status needs to be set. It does not explicitly name alternatives or exclusions, but no sibling tool appears to overlap this functionality, so explicit routing is unnecessary.

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 carry the key behavioral hints: destructiveHint=true, readOnlyHint=false, and idempotentHint=true. The description adds the target ('Cronometer diary') and the 'one or more' batch capability, but it does not disclose potential side effects, failure semantics, or irreversibility beyond what 'Remove' and the destructve annotation imply.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the purpose, followed by a short prerequisite and an Args list. Every sentence contributes necessary information and there is no filler or unnecessary 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 two simple parameters, an output schema, and annotations covering destructive/idempotent behavior, the description is complete: it states what is removed, how to find valid IDs, and how to specify the date. No critical information needed to invoke the tool correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description compensates fully by explaining both parameters: entry_ids is 'List of serving/entry IDs to remove,' and date is 'YYYY-MM-DD (defaults to today).' It adds format and default semantics that the input schema, which only declares 'string or null', does not provide.

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 action and resource: 'Remove one or more food entries from the Cronometer diary.' This clearly distinguishes it from sibling tools like edit_food_entry or add_food_entry by naming the removal operation and the target (diary entries).

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 prerequisite guidance: 'Use get_food_log to find entry IDs,' which tells the agent how to obtain the required parameter. However, it does not explicitly state when not to use this tool or compare it to alternatives such as edit_food_entry, so it lacks full exclusion guidance.

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

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

Annotations already establish the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the bar is lower. The description adds useful behavioral context beyond annotations: results contain food IDs, measure_ids, and source information, and matching is by name/keyword. There is no contradiction between the description and 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?

Every sentence earns its place: purpose, return shape, downstream routing, then the parameter definition. The purpose is front-loaded in the first sentence, and the entire description is compact with zero 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 low-complexity, single-parameter, read-only search tool with an output schema and rich annotations, the description is complete. It covers what the tool does, what results contain, how to chain results into add_food_entry or get_food_details, and what the query parameter means. Minor details like pagination or result limits are not critical at this level of simplicity.

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 fully compensate — and it does. The Args block defines query as a 'Food name or keyword' and supplies concrete examples ('eggs', 'chicken breast'), adding real meaning that the bare string parameter in the schema completely lacks.

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 opening sentence, 'Search Cronometer's food database by name,' states a specific verb, resource, and scope in a single line. The follow-up about returning food IDs and source information, plus the routing to add_food_entry and get_food_details, distinguishes it from sibling listing and retrieval tools such as list_custom_foods and find_entries_by_food.

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 downstream workflow guidance: use the returned food_id and measure_id with add_food_entry, or pass food_id to get_food_details for full nutrition info. It implies the public-database scope and separates this tool from detail-fetching tools, but it never explicitly states when not to use it (e.g., for custom foods, use list_custom_foods), leaving a small gap.

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