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

With no annotations, the description carries the behavioral disclosure burden. It does add useful behavior: adding multiple identical bottles in one row and applying remaining_percent equally to each bottle. However, it does not disclose mutation consequences, whether existing entries are checked, or what the operation returns.

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, front-loads the main action, and every clause earns its place by clarifying a meaningful behavior. There is no redundant wording or irrelevant detail.

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

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

For a mutation tool with nine parameters, zero schema descriptions, no annotations, and no output schema, this description is too sparse. It omits field semantics, likely value ranges for remaining_percent, and any success or side-effect information an agent would need to call it correctly and confidently.

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 by explaining the nine parameters. It only clarifies remaining_percent, leaving beverage_name, category, brand, bottle_volume_ml, quantity, notes, country, and variant without any semantic explanation beyond their 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 clearly states the action ('Add a row of identical bottles') and the resource (bottles), and the verb 'Add' naturally distinguishes it from sibling tools like update_bottle, update_remaining, and remove_bottle. It is clear and specific, though it does not explicitly name or contrast any sibling tool.

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

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

There is no guidance about when to use add_bottle versus alternatives such as update_remaining or seed_demo_inventory. The description implies this is the creation tool, but it never states prerequisites, exclusions, or conditions for choosing a sibling tool.

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?

With no annotations, the description carries the behavioral burden. It usefully discloses that servings are scaled from 1 to 1000 and that output is structured ingredients and steps. However, it does not mention error cases, unavailable cocktail names, or exact output formatting.

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, efficient sentence with no filler. The primary purpose is front-loaded, and the scaling constraint is presented as a compact, valuable addition.

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, but with no output schema and no annotations, the description should clarify return structure and edge behavior. It adequately conveys core function and serving range, but gaps remain around valid cocktail names, ingredient/step structure, and error handling.

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 adds meaning for the 'servings' parameter by stating the 1–1000 scaling range, which the schema does not specify. However, it provides no additional meaning for 'cocktail_name' beyond what the property name already implies.

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 ('Get') with a clear resource ('cocktail recipe') and specifies the output ('structured ingredients and steps'). This distinguishes it from sibling tools focused on inventory management and recommendations, and the unique 'recipe' scope makes confusion unlikely.

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

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

The description gives no guidance about when to use this tool versus alternatives like recommend_cocktails_from_inventory or get_inventory_summary. It states what the tool does but not the conditions that should lead an agent to select it.

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

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It does communicate that the tool returns summarized inventory data and implies a read-only operation, but it does not explain aggregation behavior, filter semantics, or what happens when no filters are supplied.

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 action and output contents. It is appropriately brief, though it is so terse that important behavioral and parameter context is omitted.

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 three optional parameters, no output schema, no annotations, and several sibling tools, the description is too incomplete. It does not explain how filters combine, whether results are aggregated globally or per filter, or when this tool should be preferred over list_inventory.

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%, and the description does not mention any of the three optional parameters (brand, category, beverage_name) or how they affect the returned summary. The description provides no added meaning beyond the parameter names themselves.

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 clear action ('Return counts, brands, variants and remaining ml') and identifies the tool as an inventory summary, distinguishing it somewhat from the sibling list_inventory by emphasizing summary-level aggregation. However, it does not explicitly contrast itself with siblings, so it is not fully differentiated.

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 on when to use this tool versus list_inventory or other inventory tools. The description implies it provides a summary, but it does not state when to choose it over alternatives or how filters should be used.

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?

With no annotations, the description bears the burden of behavioral disclosure. It adds useful matching semantics (accent-insensitive, case-insensitive, substring), but does not mention return shape, pagination, or explicitly confirm read-only behavior.

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

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

The entire description is one tight, front-loaded sentence with no filler. Every word contributes either the action or the filter behavior.

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 list with three optional, self-explanatory parameters, the description gives the core invocation details and filter semantics. Missing return-shape details are a minor gap, but the lack of output schema and annotations makes the description merely adequate rather than 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?

Schema description coverage is 0%, so the description must compensate. It conveys that all filters are optional and use substring matching with accent/case-insensitivity, giving meaning beyond the raw param names. However, it does not explain how multiple filters combine or define each parameter's exact scope.

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 and resource: 'List bottles' with optional filters, which clearly identifies the operation. It is distinct from siblings like add_bottle or get_inventory_summary, though it does not explicitly name an alternative.

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 on when to choose this tool over siblings such as get_inventory_summary or recommend_cocktails_from_inventory, and no conditions or excluded cases are given. The description only states what the tool does, not when to use it.

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

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

The description discloses the core behavior: comparing stock volumes against a one-serving baseline and distinguishing missing required versus optional items. However, there are no annotations to cover safety or side effects, and the description does not mention whether the tool is read-only, what it returns, or how it handles edge cases like insufficient stock for required items.

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 compact sentence with no filler, and it front-loads the main comparison action. It could be slightly clearer by explicitly saying it recommends cocktails, but for a zero-parameter tool it is appropriately 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 tool with no parameters, annotations, or output schema, the description states the essential logic but not the return shape or expected result format. An agent can infer that it returns cocktails with missing-item information, but the lack of any mention of output or follow-up behavior leaves a moderate 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 input schema has zero parameters, so there are no parameter details to explain. With schema description coverage at 100% and no parameters to document, the baseline of 4 is appropriate; the description need not add parameter-level semantics.

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

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

The description identifies a specific resource (cocktails from inventory) and a clear distinguishing behavior: comparing stock volumes for one serving and separating missing required from missing optional items. It does not use the verb 'recommend' and the phrasing reads more like an internal computation than a user-facing recommendation action, which keeps it from a 5.

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 explicit guidance on when to use this tool versus siblings such as get_cocktail_recipe or get_inventory_summary. The context of 'from inventory' and comparing stock volumes implies the use case, but the description does not state when to prefer this tool or when an alternative would be more appropriate.

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?

With no annotations and no output schema, the description carries the full burden of behavioral disclosure. It only says the tool ranks pairings and offers a parameter fallback hint; it does not disclose whether the operation is read-only, what happens when a beverage/brand cannot be matched, how the ranking is ordered, or what the response looks like.

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 the core action front-loaded and a usage hint added neatly after the semicolon. It avoids filler, though the phrase 'category/variety' introduces some ambiguity that a slightly more explicit phrasing could remove.

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 five parameters, no output schema, no annotations, and zero schema description coverage, this short description is not sufficient for an agent to reliably construct a correct call. The agent would not know the required beverage_name semantics, how optional parameters interact, or what result format 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?

Schema description coverage is 0%, so the description must explain the parameters. The only guidance is 'pass category/variety for unknown brands,' which is ambiguous because there is no explicit category parameter and the mapping to variant and food_category is unclear. It does not explain beverage_name, brand, limit, or food_category semantics.

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

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

The description states a specific action (rank) and resource (pairings derived from beverage characteristics), which aligns with the tool name and distinguishes it from sibling cocktail-recommendation tools. It could be slightly more explicit that the pairings are food pairings, but the intent is reasonably 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 one useful usage hint: pass category/variety for unknown brands. However, it does not explain when to choose this tool over sibling tools like recommend_cocktails_from_inventory, nor does it mention any exclusions or prerequisites. The guidance is present but minimal.

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?

With no annotations, the description must carry the behavioral disclosure. It does reveal what gets destroyed ('inventory row... including all bottles counted in its quantity'), which is valuable. However, it omits other behavioral details such as irreversibility, permission requirements, or any cascading effects beyond the quantity.

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, tightly written sentence. 'Delete an inventory row by ID' front-loads the primary action, and the qualifying phrase about bottles counted in quantity earns its place by conveying important scope. There is no 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 simple one-parameter delete tool, the description covers the core action and the destructive scope. Yet the absence of any annotations or output schema means more context would help: no mention of permanence, expected return/confirmation, or prerequisites. It is adequate 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?

Schema coverage is 0%, so the description must compensate for the undocumented 'bottle_id' parameter. It partially does so by saying deletion happens 'by ID', mapping the integer parameter to row identity. Yet it does not explicitly name 'bottle_id' or add format or constraint details, though the parameter name itself is self-descriptive.

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 ('Delete'), a specific resource ('an inventory row'), and the exact selection mechanism ('by ID'). It clearly distinguishes itself from sibling update/list/add tools, and the added scope ('including all bottles counted in its quantity') clarifies the full extent of the operation.

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

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

The description gives no context on when to use this tool versus alternatives like update_bottle or update_remaining. There are no conditions, exclusions, or any 'use this when...' guidance, so the agent is left to infer usage from the verb 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?

With no annotations, the description carries the full burden. It discloses two behavioral traits: the operation is one-time ('once') and it refuses to run when unseeded inventory exists. However, it does not clarify side effects (e.g., whether it wipes existing data), idempotency, or exactly what happens when the refusal is triggered. Some transparency is present, but significant behavioral gaps remain.

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, tightly worded sentence. Every phrase adds information: 'Explicitly' signals deliberate action, 'synthetic data' names the payload, 'once' gives frequency, and the refusal clause states a hard constraint. 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?

The tool is simple (one optional boolean parameter, no output schema), but the description is still incomplete. It does not explain the allow_demo parameter, what 'unseeded inventory' means, what the return value is, or what happens on a refusal. An agent would need to guess or inspect other tools to use 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?

The schema exposes one parameter, allow_demo, with no description, and schema_description_coverage is 0%. The tool description never mentions this parameter, so an agent cannot determine what allow_demo controls or when to set it to true. The description provides zero value for parameter understanding.

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 ('seed') with a clear resource ('demo inventory') and a distinguishing scope ('synthetic data once'). It also names a behavioral constraint ('refuses to mix with existing unseeded inventory') that separates it from sibling tools like add_bottle or update_bottle, which operate on real inventory 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 phrase 'Explicitly seed synthetic data once' gives clear context for when to use the tool: a one-time demo setup. The refusal clause adds an implicit condition of use - it should not be run when inventory already contains unseeded data. However, it does not explicitly name alternatives or contrast with sibling tools such as add_bottle, so an agent must infer the routing.

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?

With no annotations, the description carries the full burden of behavioral disclosure. It does disclose that unknown fields and invalid values are rejected, which is useful, but it does not explain success/failure behavior, whether the update is partial, whether it validates the required `bottle_id` existence, or what the response looks like. A mutation tool with no annotation coverage needs more than this.

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 compact sentence with no filler. The primary action is front-loaded and the validation behavior is stated in a secondary clause. Every word earns its place, even though more content is needed overall.

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 an opaque nested `changes` object, no output schema, no annotations, and a similarly named sibling `update_remaining`, this description is incomplete. The agent cannot reliably construct a valid call because the editable fields and the expected `changes` object format are never specified.

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 provides 0% description coverage, so the description must compensate. It mentions 'editable BottleInput fields' but does not enumerate those fields, explain the structure of the `changes` object, clarify acceptable value formats, or describe the role of `bottle_id`. The agent is left guessing at the shape of the required `changes` parameter.

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 operation ('Update') and resource ('editable BottleInput fields'), and it adds a useful validation detail ('rejects unknown fields and invalid values'). However, it does not explicitly differentiate itself from the sibling tool `update_remaining`, which likely has overlapping update semantics.

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 alternatives such as `update_remaining` or `add_bottle`. The description states what the tool does but gives no context for choosing it over sibling tools, 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the mutating action 'Set' and the percentage range, but it does not mention validation of out-of-range values, whether existing values are overwritten, side effects, or any permission 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 sentence with no filler, and the core verb and resource are front-loaded. The phrasing 'each bottle represented by this row' is somewhat awkward, but the overall structure is efficient and readable.

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 update, the description covers the essential operation and the percentage range. However, with no annotations and no output schema, it omits context about return behavior, error handling, and how this tool fits among the bottle-management siblings, making it minimally sufficient but not 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?

Schema description coverage is 0%, so the description must compensate. It gives meaning to remaining_percent by specifying a 0–100 range, but bottle_id is only loosely implied by 'each bottle represented by this row' and not explicitly identified as the target identifier. The compensation is partial at best.

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 'Set' and names the resource 'remaining percentage' with a clear 0–100 range, making the tool's primary function obvious. However, the phrase 'each bottle represented by this row' is slightly ambiguous and does not explicitly differentiate it from the sibling update_bottle.

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 update_bottle or any other sibling. It does not mention conditions, exceptions, or alternatives, leaving usage entirely to inference.

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