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

No annotations are provided, so the description must carry full behavioral disclosure. It fails to specify what the engine returns (string enum, object with reasoning?), whether it stores decision history, or if it has any side effects. 'Eliminates the decision' is vague about actual output format.

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 that are appropriately front-loaded and thematic. No wasted words, though the playful marketing style slightly sacrifices functional clarity for tone.

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 and no annotations, the description should disclose the return value (recommendation vs order placement) and structure. It omits this critical information, leaving the agent uncertain about what the 'engine' produces.

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 100%, establishing a baseline of 3. The description adds no explicit parameter semantics, though the playful tone ('agonizing decision') implicitly contextualizes why inputs like clothing_color and risk_tolerance matter (spill risk) without explicit mapping.

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?

States it is a 'format selection engine' for 'fast casual dining' and references 'the most agonizing decision,' which combined with the tool name clearly signals this selects between bowl and burrito formats. However, it relies heavily on the tool name and domain context rather than explicitly stating both options in the description text.

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 no guidance on when to use this versus siblings like build_entree or customize_order. Does not indicate whether this should be called before ordering or if it automatically places an order.

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 provided, so description carries full burden. 'Build' implies creation but doesn't disclose what gets returned (entree ID? object?), whether this persists to a cart/session, if it's idempotent, or side effects. Missing behavioral contract for a mutation tool.

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

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

Two sentences with no redundancy. However, given lack of annotations and output schema, the brevity leaves gaps; a third sentence covering return value or workflow placement would improve utility without sacrificing 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?

With 6 parameters (5 required) and 100% schema coverage, input side is well-covered by schema. However, zero annotations and missing output schema means description should explain the return value or state change. It doesn't, leaving behavioral context incomplete for a mutation tool of this 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?

Schema description coverage is 100%, so baseline applies. Description generically references 'preferred ingredients' which loosely maps to parameters but adds no specific syntax guidance, validation rules, or examples beyond the exhaustive schema documentation.

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

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

Clear verb 'Build' with specific resource 'Chipotle entree', and 'core of any order' distinguishes from auxiliary siblings like nutrition facts or line forecasting. However, it doesn't clarify distinction from 'customize_order' or 'place_order' in the workflow.

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?

States 'This is the core of any order' implying when to use (when constructing an order), but lacks explicit when-NOT-to-use guidance or workflow sequencing relative to siblings like 'customize_order' or 'place_order'.

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 full behavioral disclosure burden. It effectively explains the methodology ('proprietary tortilla stress models') and the prediction target ('structurally fail'). However, it omits crucial execution details: what the output format is (boolean, probability score, failure mode description?), whether this requires network calls, and any rate limits or side effects.

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

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

Single sentence with zero waste. 'Proprietary tortilla stress models' establishes domain expertise, 'structurally fail' defines the risk category, and 'during consumption' specifies the temporal scope. Every phrase adds unique semantic value beyond the tool name.

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 whimsical domain, the description adequately explains the physics-based prediction purpose. With 6 well-documented input parameters but no output schema, it successfully covers the 'what it does' aspect but could benefit from a brief note on return value format (integrity score vs. pass/fail) to be 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 description coverage is 100%, with all 6 parameters fully documented in the schema. The description does not mention parameters, but with such high schema coverage, the baseline expectation is met. The description focuses on the prediction capability rather than re-documenting ingredient parameters.

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 uses specific verb 'predict' and clarifies the exact resource domain (burrito structural integrity via 'tortilla stress models'). It clearly distinguishes from siblings like 'salsa_risk_assessment' (spice/ingredient risk) and 'bowl_vs_burrito_decision_engine' by focusing specifically on physical structural failure during consumption.

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 this is a pre-consumption check ('during consumption'), suggesting when to use it. However, it lacks explicit guidance on when to prefer this over 'build_entree' (which presumably validates ingredients) or whether this should be called before or after building the order. No alternative tools 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?

With no annotations provided, the description carries full behavioral disclosure burden. While 'Check' suggests a read operation, the description fails to disclose what status values are returned, whether this operation is idempotent, or if there are rate limits on checking status.

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?

Single front-loaded sentence with zero waste. Every word earns its place by defining the action, target, and scope 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?

Adequate for a single-parameter tool with full schema coverage, but lacks description of return values or status enumerations (e.g., pending, ready, completed) which would help given no output schema exists.

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 100% (order_id fully documented with example format CHIP-1001), establishing baseline 3. The description adds no parameter-specific context, but none is needed given the schema's completeness.

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?

Clear verb 'Check' and resource 'status of a previously placed order'. The phrase 'previously placed' effectively distinguishes this from the sibling 'place_order' tool by implying this operates on existing orders, though it doesn't explicitly name alternatives.

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 implied usage context via 'previously placed', suggesting this is for post-purchase tracking. However, lacks explicit when-to-use guidance (e.g., 'Use after place_order') or conditions for when status cannot be retrieved.

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 disclosure burden. It establishes that the tool returns a personality classification ('what kind of Chipotle eater you are'), providing basic behavioral context. However, it omits side effects, data persistence, whether results are deterministic, or any rate limiting concerns.

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 zero waste. 'Scientifically' and 'what kind of Chipotle eater you are' front-load the value proposition immediately, and every word contributes to understanding the tool's whimsical analytical purpose.

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 input schema is fully documented, the absence of an output schema creates a gap the description doesn't fill. It states the tool 'determines' a personality type but doesn't describe the return format (string, object, category), confidence levels, or whether results include explanations. Given 4 required parameters and no annotations, additional output specification 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?

Schema description coverage is 100%, documenting all four parameters ('Your usual protein', 'How often do you get guac?', etc.). The description aggregates these as 'order preferences' but adds no additional syntax details, constraints, or interdependencies beyond what the schema already provides, warranting the baseline score.

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 ('determines') and clearly identifies the resource (Chipotle eater personality type). It effectively distinguishes this analytical/entertainment tool from functional siblings like 'place_order', 'check_order_status', and 'build_entree' by focusing on preference analysis rather than transactional operations.

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

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

The description implies usage through the phrase 'based on your order preferences', suggesting it's for analyzing ordering habits. However, it lacks explicit guidance on when to select this versus analytical siblings like 'optimize_guac_roi' or 'bowl_vs_burrito_decision_engine'. No exclusions or prerequisites 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. 'Results may vary' vaguely hints at uncertainty but fails to explain what the tool actually does (e.g., whether it modifies order state immediately, submits a request for approval, or notifies staff), nor does it mention side effects, permission requirements, or idempotency.

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 appropriately brief with only two sentences and no verbosity. The key purpose is front-loaded in the first sentence. However, the second sentence ('Results may vary.') is vague and does not earn its place effectively, preventing a score of 5.

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?

As a mutation tool with no annotations and no output schema, the description is insufficiently complete. It fails to explain success indicators, failure modes, or what 'customize' entails operationally, leaving developers without critical context for a state-changing 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?

The input schema has 100% description coverage ('The order ID to customize', 'Your special request'), establishing clear semantics for both parameters. The description adds no parameter-specific guidance beyond the schema, which warrants the baseline score of 3 for high-coverage schemas.

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 clear verb ('Make') and resource ('special requests for your order'), establishing what the tool does. However, it does not explicitly differentiate from siblings like 'place_order' or 'build_entree' that may also involve customization, leaving potential ambiguity about whether this modifies existing orders versus creates new ones.

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 over alternatives like 'place_order' or 'build_entree'. The phrase 'Results may vary' offers no actionable criteria for tool selection and does not clarify prerequisites (e.g., requiring an existing order).

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. 'Protects' vaguely suggests a warning or advisory function but fails to specify the mechanism (Does it return a boolean? A warning message? Block subsequent orders?) or disclose side effects, auth requirements, or what constitutes 'excessive'.

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 with zero waste. The first sentence front-loads the core function ('Protects users'), while the second provides categorical context ('A wellness tool'). Every word earns its place; no repetition of schema details or tautology with the name.

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?

Input parameters are fully documented by the schema (100% coverage), but the description omits crucial information about the tool's output (what constitutes a 'protection' response? Is it a string warning, a boolean, or an action?) Given the lack of output schema and annotations, this gap in behavior specification leaves the tool's effect 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 coverage is 100%, establishing a baseline of 3. The description adds valuable semantic context by framing the numeric inputs within a 'wellness' and 'burrito-dependent' narrative, explaining why tracking 'times_this_week' matters (health protection), which helps the agent understand the domain constraints beyond raw parameter types.

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 ('Protects') and clearly identifies the resource (Chipotle consumption) and scope ('excessive'). It successfully distinguishes this wellness/limiting tool from siblings like 'place_order' or 'build_entree' by emphasizing consumption control rather than acquisition or preparation.

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?

While the phrase 'burrito-dependent' implies a target user persona, there is no explicit guidance on when to invoke this tool versus alternatives (e.g., 'use before placing an order' or 'use for health tracking'). No 'when-not' exclusions or prerequisite conditions 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?

No annotations provided, so description carries full disclosure burden. Beyond the ironic hint that data may be fabricated, it lacks critical behavioral details: no mention of what happens when items don't exist, response format/fields (calories, macros), caching, or rate limits.

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?

Single sentence, appropriately brief. The parenthetical joke adds character fitting the server's humorous theme (siblings include 'chipotle_personality_test', 'burrito_integrity_check') without severely harming clarity. Front-loaded with action verb.

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?

Adequate for a simple single-parameter read operation. Lacks output description (common for tools without output schemas), but mentions the core entity (nutrition facts). Could be improved with error case documentation.

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 100% with the 'item' parameter fully described. Description reinforces this by mentioning 'menu item' but adds no additional syntax guidance, format examples, or validation rules beyond the schema. Baseline 3 appropriate for high-coverage schemas.

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?

States specific verb (Get) and resource (nutrition facts) with clear scope (menu item). The humorous parenthetical indicates the data source may be mock/fake, which actually clarifies behavior. Lacks explicit differentiation from sibling 'view_menu', though the focus on nutrition vs. general menu viewing is distinct.

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 no guidance on when to use this tool versus alternatives like 'view_menu', or prerequisites (e.g., needing to know item names from menu first). No error handling guidance included.

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 provided, so description carries full burden. Mentions 'ML-powered' but fails to disclose critical behavioral traits: return format (minutes? confidence intervals?), location scope (generic vs specific store), prediction reliability, or data staleness. No mention of rate limits or calculation latency.

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 efficient sentences with purpose front-loaded. Minor deduct for 'advanced algorithms' which adds no specific value. Structure is appropriate for the tool complexity.

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?

Adequate for a 3-parameter tool with complete schema coverage, but lacks output description critical for a forecasting tool (what units? range?). Missing location context is notable given the domain.

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 100% with clear descriptions for all 3 parameters. Description adds 'burrito congestion' metaphor but provides no additional semantic guidance (e.g., explaining 'is_payday' logic or default rationales) 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?

Clear verb+resource ('estimate... wait times') and distinguishes from siblings (ordering/nutrition tools). 'Burrito congestion' is colloquial but meaning is decipherable. Lacks explicit 'predicts' framing that would clarify it's a forecast vs real-time check.

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 explicit guidance on when to use vs alternatives (e.g., should this be called before place_order?). No mention that all parameters are optional with sensible defaults, which affects invocation strategy.

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 provided, so description carries full burden. It establishes the tool performs an economic calculation ('economically rational'), but omits side effects, output format, or whether this stores decision history. Does not contradict any structured data.

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?

Single sentence, front-loaded with active verb. 'Economically rational' and 'financial and emotional state' efficiently convey the decision criteria without waste. No redundant phrases.

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?

Three well-documented parameters (100% coverage) but no output schema provided. Description should ideally specify what determination is returned (boolean, cost-benefit analysis, recommendation) to complete the contract. Adequate but gap exists.

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 100%, establishing baseline 3. Description adds conceptual framing by mapping 'bank_balance' to 'financial state' and 'hunger_level' to 'emotional state,' providing semantic glue between raw parameters and the ROI concept.

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?

Specific verb ('Determines') and clear resource scope ('adding guacamole'). Uniquely distinguishes from siblings like 'customize_order' or 'place_order' by focusing on economic rationality calculation rather than order execution.

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?

Implies usage context through 'given your current financial and emotional state' (i.e., when deciding about guac), but lacks explicit when-to-use guidance or named alternatives like 'customize_order' for users who've already decided.

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 full burden of behavioral disclosure. It fails to mention critical transactional behaviors: whether this charges money, if the order is immediate or scheduled, whether it is reversible/cancellable, or what confirmation/tracking mechanism follows. 'Place' implies commitment but lacks transparency about side effects.

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

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

The two-sentence structure is appropriately brief and front-loaded with the core action. However, the second sentence spends space listing parameter categories that are already obvious from the schema, rather than providing usage constraints or behavioral warnings.

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?

As a 9-parameter transactional tool with no annotations and no output schema, the description is incomplete. It omits critical context for an order placement operation: payment implications, order confirmation details, pickup/delivery logistics, or error handling. For a destructive financial transaction, this lack of behavioral context is a significant 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?

With 100% schema description coverage, the schema already documents all 9 parameters clearly. The description provides only a high-level grouping ('entree details, optional sides, and drinks') without adding syntax guidance, validation rules, or semantic relationships between parameters (e.g., that certain proteins cost extra with double_protein). Baseline 3 is appropriate.

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

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

The description uses a specific verb ('Place') and clear resource ('Chipotle order'), and the word 'complete' helps distinguish this from sibling tools like 'build_entree' or 'customize_order'. However, it could more explicitly clarify the distinction between assembling an order versus finalizing/committing it.

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

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

The description mentions what inputs to provide ('entree details, optional sides, and drinks') but offers no guidance on when to use this tool versus siblings like 'build_entree', 'customize_order', or 'bowl_vs_burrito_decision_engine'. No prerequisites, conditions, or alternatives 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?

No annotations provided, so description carries full burden. States 'automatically configures' but fails to disclose what system state changes occur (creates draft order? saves preference? returns recommendation only?), auth requirements, or side effects.

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

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

Extremely brief (two sentences). Front-loaded with functional description first; second sentence ('Gains-as-a-Service') is promotional fluff but doesn't significantly detract from parsability.

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?

Adequate for simple 2-parameter tool with 100% schema coverage, but ambiguity about relationship to order flow (configuration vs. placement) and no output disclosure leaves gaps given the complex sibling ecosystem.

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 has 100% description coverage ('What workout did you just do?', 'Current fitness goal'), establishing baseline 3. Description adds conceptual link between workout and nutrition but does not augment parameter syntax, format, or validation details beyond 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?

Clear specific verb ('configures') and resource ('burrito') with domain context ('based on your workout'). Implicitly distinguishes from generic sibling 'build_entree' by focusing on post-workout nutrition optimization, though explicit differentiation is absent.

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 implied usage context (post-gym meals) via 'based on your workout', but lacks explicit when-to-use guidance, prerequisites, or named alternatives among the 13 sibling tools like 'build_entree' or 'customize_order'.

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 full burden. It discloses the predictive nature of the operation and adds crucial medical context via the doctor consultation warning. However, it omits details about data persistence, calculation methodology, or specific output format.

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 with zero waste. The first sentence front-loads the purpose; the second provides necessary medical disclaimer. Every word earns its place with no repetition of structured data.

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?

Appropriately complete for a 3-parameter tool with 100% schema coverage and no output schema. The medical disclaimer adds necessary safety context for a health-adjacent prediction tool, though it could briefly characterize the prediction output (e.g., risk level vs detailed report).

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 100% (all 3 parameters have descriptions). The description references 'your salsa choices' aligning with the salsas parameter but does not add semantic detail beyond what the schema already provides, warranting the baseline score.

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?

Clear specific verb 'Predicts' with defined scope 'gastrointestinal consequences of your salsa choices'. Uniquely distinguishes from sibling ordering/nutrition tools by focusing on health risk assessment rather than food selection or nutritional 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?

Implied usage through the specific health domain ('gastrointestinal consequences', 'consult your doctor'), suggesting use when evaluating spice-related health risks. Lacks explicit comparison to siblings like get_nutrition_facts or when-not 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?

No annotations are provided, so the description carries the full burden. It implies read-only behavior through the verb 'View' but does not describe what data structure is returned, whether the menu is cached/real-time, or any authentication requirements. Adequate but minimal disclosure for an external-facing tool.

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

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

Two sentences with zero wasted words. The action ('View the full Chipotle menu') is front-loaded, followed immediately by usage context ('Start here before ordering'). 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?

Given the tool's zero-parameter simplicity and lack of output schema, the description adequately covers the essential intent and entry-point guidance. While it could optionally describe the returned menu structure, this omission is minor given the self-evident nature of a 'view_menu' 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?

The tool accepts zero parameters. According to evaluation standards, zero-parameter tools receive a baseline score of 4, as there are no parameter semantic gaps to fill.

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 exactly what the tool does ('View') with a specific resource ('the full Chipotle menu'). The phrase 'full' distinguishes this comprehensive view from sibling tools like 'build_entree' or 'customize_order' which work with specific items.

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 'Start here before ordering' provides explicit workflow sequencing, indicating this is a prerequisite step before using sibling tools like 'place_order' or 'build_entree'. While it doesn't explicitly name alternatives, it clearly signals when to invoke this tool in the task lifecycle.

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