UK Data: Code utilities

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

The annotations already establish that this is a read-only, idempotent, non-destructive operation. The description adds useful context beyond them: input and output are binary strings, carry propagation is performed, and the call costs 1 credit.

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 definition is short and front-loads the operation and return type. The carry-propagation sentence is mildly redundant with the first sentence, but it remains compact and does not obscure the core instruction.

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 pure computation with full schema coverage, safety annotations, and no output schema, the description supplies everything needed to invoke it correctly. Nothing material about the operation 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 coverage is 100%, and the schema documents both parameters with binary-string examples. The description confirms that both operands are strings, but it does not add syntax, formatting, or edge-case details beyond what the schema already provides.

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 names a specific operation and resource: adding two binary numbers represented as strings and returning a binary-string sum. This is unambiguous and clearly distinct from neighboring utility tools such as count_bits, binary_search, or is_power_of_two.

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?

Usage is implied by the precise purpose: call this when binary-string addition is needed. However, the description gives no explicit when-to-use guidance, no exclusions, and no named alternatives for related calculations.

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 readOnly, idempotent and non-destructive, so the safety profile is covered. The description adds genuinely useful behavior not in the structured fields: the return value (the index) and the not-found sentinel of -1, plus a credit cost. It omits edge behavior such as duplicate handling or what happens on unsorted input.

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 tight sentences with no filler: the core action and return contract come first, and the credit cost is appended compactly. Nothing should be cut.

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

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

With no output schema, the description correctly carries the return-value contract (index or -1), which is the most important thing an agent needs. Remaining gaps are minor: sort direction, duplicate handling, and behavior on unsorted input.

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 both parameters described, so the baseline of 3 applies. The description reinforces that the array must be sorted (a constraint the schema description also mentions) but adds no further semantics on types or ordering.

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?

Names a specific verb (performs binary search) and resource (sorted list), and states the outcome (index of target, -1 if absent). It is distinguishable from most siblings, though it never contrasts itself with adjacent algorithm tools like find_kth_smallest or sort_numbers.

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 'sorted list' implicitly gives the key precondition for use, but there is no explicit when-to-use/when-not guidance and no pointer to alternatives such as sort_numbers for unsorted input. Adequate but leaves inference to the agent.

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 readOnly, idempotent, non-destructive and open-world behavior, so the safety profile is covered. Beyond that, the description adds two genuinely useful facts: the exact return contract (true when no conflicts, false otherwise) and the cost of 1 credit. It doesn't describe edge cases like touching endpoints or input format expectations, which keeps it short of a 5.

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?

Three short sentences, front-loaded with the purpose, then the return contract, then the cost. The phrase 'by checking for overlapping time slots' is mildly redundant with the rest but does clarify the underlying logic. No filler or hedging language.

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 single-parameter boolean predicate with no output schema, the description supplies everything essential: what it computes, what it returns, and its cost. The only real gap is its silence on accepted interval formats, which the schema leaves 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 100%, so the interval parameter is already documented in the schema, and the description only echoes 'list of time intervals'. Notably it does not resolve the schema's ambiguity (array of strings or a raw string) or show an example interval encoding, so it adds no meaning beyond the structured field.

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 outcome — determine whether a person can attend all meetings from a list of intervals — which is far more than a restatement of the name. It makes the boolean decision explicit. It does not, however, distinguish itself from the related sibling min_meeting_rooms, which an agent could easily confuse it with.

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 versus alternatives such as min_meeting_rooms, and no statement of preconditions or exclusions. The only implied usage is 'check a schedule for conflicts', which the agent must infer from the mechanics 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 readOnly, idempotent, non-destructive behavior, so the safety profile is covered. The description adds information that is nowhere in the structured fields: the operation costs 1 credit, which matters for an agent deciding whether to call it. It also confirms the domain restriction (non-negative input), reducing error risk.

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 short sentences, front-loaded with the operation and resource, with the cost note parenthetically appended. Nothing is redundant or padding.

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?

This is a simple, single-parameter pure function, and the description plus schema fully specify how to call it. The only omission is the return type (the bit count as an integer), which is obvious from the tool's name and there is no output schema to compensate, but the gap is minor for an operation this elementary.

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%, and the single 'num' parameter is already documented as a required non-negative integer. The description restates the non-negative constraint but adds no format, range, or edge-case detail (e.g. behavior for zero, or maximum bit width) beyond the schema. Baseline 3 applies when the schema carries the parameter 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 names a specific verb (counts) and a precise resource (set bits / 1's in the binary representation), removing any ambiguity about what the tool returns. It does not explicitly distinguish itself from nearby computational siblings like is_power_of_two or binary_addition, but the operation is distinct enough that an agent can select it without opening the schema.

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 prefer this tool over alternatives (e.g. binary_addition, is_power_of_two, or a general 'calculate' tool), and no when-not-to-use conditions. The only usage constraint is implicit in the domain restriction to non-negative integers, which is also in the schema.

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 cover the safety profile (readOnly, idempotent, non-destructive), so the bar is lower, yet the description still adds real context: the one-duplicate precondition, the returned value, and a per-call cost of 1 credit. It stops short of saying what happens on invalid input (no duplicate or multiple duplicates), which keeps it from a 5.

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?

Three short sentences, each earning its place: the operation and precondition first, the return value second, the credit cost last. Nothing redundant and the important constraint is front-loaded.

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

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

For a one-parameter pure-computation tool with rich annotations and no output schema, the description covers purpose, precondition, output, and cost. The only missing piece is error behaviour when the input does not contain exactly one duplicate, which is a minor but real 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?

Schema coverage is 100% for the single parameter, so the baseline is 3. The description does add that the list contains integers with exactly one duplicate, which sharpens the schema's looser "array of strings or string" typing, but it gives no format, separator, or size guidance for the string form.

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 a specific verb and resource ("Finds the duplicate number in a list of integers") plus the key precondition that exactly one number repeats, which distinguishes it from broader siblings like find_majority_element or find_missing_number. It is clear on its own, though it never names a sibling or explicitly rules out near-neighbours such as find_missing_number.

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 precondition "where exactly one number appears more than once" implies the correct input context, but there is no explicit when-to-use/when-not-to-use guidance and no mention of alternatives for lists that violate that precondition. Usage is inferable rather than stated.

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 read-only, idempotent, non-destructive behavior, so the bar is lower. The description still adds real value beyond them by disclosing the -1 sentinel return for the no-solution case and the 1-credit cost, both of which affect how an agent interprets results and budgets calls.

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 tightly written sentences: the operation and its condition first, then the failure return and cost. Nothing is redundant and the most important information is front-loaded.

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

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

For a simple, deterministic computation with no output schema, the description supplies the definition and the failure sentinel, which is most of what an agent needs. The remaining gap is the accepted input encoding, given the schema's string-or-array union.

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% for the single parameter, so the schema carries the load and baseline 3 applies. The description adds nothing about accepted input forms, notably that the schema permits a plain string as well as an array, which is a non-obvious quirk an agent could get wrong.

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 names a specific operation and defines the equilibrium index precisely (left sum equals right sum), so an agent knows exactly what is computed. It does not differentiate itself from adjacent array-algorithm siblings like find_peak_element or find_majority_element, but the concept itself is self-disambiguating.

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 when-to-use or when-not-to-use guidance and no mention of sibling tools. Usage is only implied by the mathematical definition, which is unambiguous but leaves the agent to infer applicability.

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 establish that this is a read-only, idempotent, non-destructive operation. The description adds meaningful behavioral details: the null result when all characters repeat and the one-credit cost.

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

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

The description is two short, front-loaded sentences plus a cost note. Every element contributes directly to understanding the operation and its result.

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 pure function, the description covers the core operation and the important all-characters-repeat outcome. Minor edge cases such as empty-string behavior or character encoding are not addressed, but no output schema is expected to compensate.

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 is only one parameter and the schema fully describes it as the input string to search. The description does not add constraints such as case sensitivity, whitespace handling, or Unicode behavior, so it matches the baseline for complete schema coverage.

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

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

The description states a precise verb and resource: finds the first character in a string appearing only once. It also defines the negative result, so the tool is immediately distinguishable from neighboring algorithm utilities.

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?

Usage is implied clearly by the task itself, and the null-return edge case adds useful context. However, the description does not explicitly say when to choose this tool over alternatives or mention related tools.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context about 1-indexing and a cost of 1 credit, but does not disclose error behavior such as what happens if k is out of range.

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

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

The description is a single front-loaded sentence with the cost note appended. Every element earns its place: the core operation, indexing convention, k=1 example, and cost.

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 algorithmic tool with full schema coverage and rich annotations, the description is nearly complete. It could mention edge-case behavior for invalid k values, but no output schema exists and return semantics are reasonably implied.

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 both k and nums. The description reinforces that nums is an unsorted list and clarifies that k=1 returns the smallest element, but adds no syntax or format details beyond what the schema provides.

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: finding the kth smallest number in an unsorted list. It clarifies 1-indexing and the k=1 case, making the operation unambiguous, though it does not explicitly contrast itself with sibling tools such as find_n_largest or sort_numbers.

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

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

The description gives no guidance on when to use this tool versus alternatives. It implies applicability to unsorted lists but does not state when-not to use it, nor mention sibling tools that could handle related 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 mark this as read-only, idempotent, non-destructive, and open-world. The description adds two pieces of behavior beyond annotations: tie-breaking returns the first occurrence, and each call costs 1 credit. It does not define word boundaries, punctuation handling, empty-string behavior, or case sensitivity, which are relevant gaps for a string-processing 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, front-loaded with the core behavior and followed by the tie-break and cost. There is no unnecessary wording.

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 one-parameter utility with no output schema and rich safety annotations, the description covers the return value, tie-break rule, and cost. It remains slightly incomplete about tokenization and edge cases, but is adequate for correct typical invocation.

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%, so the single text parameter is already documented in the schema. The description refers only to 'a given text string' and adds no format, encoding, or tokenization semantics 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 states a specific verb ('Finds and returns') and resource ('the longest word in a given text string'), and it adds tie-breaking behavior. This is enough for an agent to distinguish it from sibling string utilities such as reverse_string or text_stats.

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 provides no explicit when-to-use guidance, no alternatives, and no exclusions; the usage is only implied by the purpose. An agent can infer it is for extracting the longest word, but the description does not route it against related string tools.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is covered. The description adds genuinely new behavioural detail beyond that: the exact algorithm (Boyer-Moore voting), the null return when no majority exists, and a credit cost of 1. It stops short of covering edge cases such as empty input or ties, which keeps it off a 5.

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?

Three short sentences, front-loaded with the core action, then return semantics, then cost. Nothing is redundant and every clause carries information the agent needs.

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

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

With no output schema, the description correctly takes on the burden of explaining the return value (element or null), and it also discloses cost and algorithm. For a one-parameter pure function this is nearly sufficient; only edge-case behaviour (empty list, multiple candidates) is left unstated.

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% for the single `nums` parameter, so the baseline of 3 applies. The description adds the majority-element condition the list is being tested against, but gives no format or size guidance, and notably the schema allows a string or an array of strings while the prose says "list of integers" — a mismatch the description does not resolve.

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 gives a specific verb and resource ("finds the majority element in a list") and even defines the mathematical criterion (appears more than ⌊n/2⌋ times) plus the algorithm used. It is unambiguous on its own, but it does not distinguish itself from near-neighbour tools such as find_duplicate_number or find_missing_number, which an agent scanning the sibling list might confuse it with.

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?

Usage is only implied: the definition of majority element tells an agent what input qualifies, but there is no explicit when-to-use or when-not-to-use guidance and no named alternative for related array-search tasks. The agent must infer that this is the right tool for majority-frequency queries.

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 (readOnly, idempotent, non-destructive), so the description only needs to add context beyond that — and it does: naming Kadane's algorithm signals the O(n) single-pass behavior and that overlapping subarrays are considered, while the '(cost: 1 credit)' note discloses a billing trait found nowhere in 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?

A single front-loaded sentence covering what the tool does plus a short cost tag; there is no padding or redundancy. The credit note is slightly orthogonal to the core operation but is genuinely useful and costs only a parenthetical.

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 one-parameter, annotation-covered tool this is close to sufficient, but with no output schema the description leaves the return value underspecified — it implies a numeric maximum sum but never says whether the subarray indices are returned, nor how empty or all-negative inputs are handled.

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 the input contract is already documented in structured form and the baseline is 3. The description adds nothing about the 'nums' parameter — notably it does not clarify the mismatch between the schema's string/array-of-strings typing and the documented 'list of integers'.

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 — 'Finds the maximum sum of any contiguous subarray' — and even names the algorithm (Kadane's), which unambiguously separates it from sibling find_* tools like find_peak_element or find_majority_element. It stops short of explicitly contrasting with those siblings, so it is clear but not maximally 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?

The narrow, single-purpose name makes the intended use obvious by implication, but the description offers no explicit when-to-use guidance, no statement of when not to use it, and no pointer to any alternative tool. Adequate but with clear gaps.

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 readOnly, idempotent, non-destructive behavior, so the safety profile is covered. The description adds genuinely new context: the O(log n) binary-search approach and a 'cost: 1 credit' charge, which matters for invocation budgeting. It stops short of noting edge cases such as empty or non-rotated input.

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?

Three short sentences: purpose first, then the input precondition, then the cost note. Nothing is redundant and the most important information is front-loaded.

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

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

For a one-parameter computational tool with full schema coverage and no output schema, the description covers what it computes, the input shape it expects, and its credit cost. Minor gaps remain around degenerate inputs (empty array, non-rotated array) and the exact return shape.

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%, so the single 'nums' parameter is already documented in the schema. The description reinforces the input contract by specifying it must be an ascending-sorted array rotated at a pivot, which is useful validation context but not much 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?

States a specific verb and resource ('Finds the minimum element in a rotated sorted array') and adds the algorithmic method ('using binary search'). This clearly separates it from siblings like find_peak_element, binary_search, and sort_numbers, which an agent could otherwise confuse with 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 precondition 'originally sorted in ascending order then rotated at some pivot point' implicitly tells the agent when this tool applies, but there is no explicit steering versus alternatives (e.g. using sort_numbers or a general min) and no statement about when not 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?

Annotations already declare readOnly, idempotent, non-destructive and openWorld, so safety is covered. The description adds the notable behavioral detail of a 1-credit cost and the algorithmic approach, but says nothing about output form or failure behavior when the precondition is violated.

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?

Front-loaded with the core definition in the first sentence, then constraint, then cost. 'Uses sum formula or XOR method' is implementation detail with limited value to a caller, the one mildly wasteful sentence.

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 one-parameter read-only computation with no output schema, the definition supplies the precondition and cost that an agent needs. The only gap is not stating the return value's form (the missing integer, or what happens when none 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 coverage is 100% and the single param is documented there, but the description adds real meaning the schema lacks: the values must span 0..n and exactly one must be absent. That constraint is what makes the parameter valid input rather than just an array of numbers.

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?

Names a specific operation and resource — finding the single missing integer in a 0..n sequence — with the constraint 'exactly one number is missing', which makes it naturally distinguishable from siblings like find_duplicate_number or find_majority_element.

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 'exactly one missing, range 0..n' precondition effectively defines when the tool applies and rules out other sequence puzzles, but there is no explicit routing to alternatives (e.g. find_duplicate_number) or statement of when not 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?

Annotations already declare the operation as read-only, idempotent, open-world, and non-destructive. The description adds useful behavioral details beyond annotations: the -1 fallback when no greater element exists and the 1-credit cost. It does not cover edge cases such as duplicates or empty input, so it stops short of a 5.

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 three compact sentences, front-loads the core algorithm, and includes the return fallback and cost without wasted wording. Every sentence adds useful 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 low-complexity single-parameter computation, the description covers the operation, fallback return value, and credit cost. It does not fully specify the output shape or clarify the schema's string/array input union, which keeps it from a 5.

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 the single nums parameter is already documented in the schema. The description refers to a list but adds no syntax, format, or edge-case meaning beyond the schema, making the baseline 3 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 states a specific algorithm with a clear verb and resource: it finds the next greater element to the right for each list element. This clearly distinguishes it from other algorithmic siblings such as find_peak_element or find_majority_element.

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 explains what the tool computes but gives no explicit guidance on when to use it versus alternatives. There are no exclusions, prerequisites, or sibling comparisons.

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 readOnly, idempotent, and non-destructive behavior, so the safety profile is covered. The description adds two pieces of context the annotations cannot: a per-call credit cost and that results come back in descending order. It says nothing about edge cases such as n exceeding the list length or duplicate values.

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 short sentences, front-loaded with the operation and result ordering, with the cost note appended. Nothing is wasted and nothing needs to be re-read.

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 two-parameter read-only utility with full schema coverage and no output schema, the description supplies what matters: the operation, the result ordering, and the cost. Only edge-case behavior (n larger than list size, ties) is left unaddressed.

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%, so the baseline is 3; the description goes slightly beyond by tying n to 'largest' selection and clarifying that the input is a numeric list. It adds no interpretation of the string-typed schema fields, but the schema already documents both 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?

States a specific verb and resource ('Finds the n largest numbers from a list') plus the ordering of results, which is enough to separate it from siblings like find_kth_smallest, sort_numbers, or average. It stops short of naming those alternatives explicitly.

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

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

No guidance on when to choose this over the many adjacent numeric siblings (find_kth_smallest, sort_numbers, average). The use case is inferable from the description but no conditions, exclusions, or alternatives are stated.

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, destructiveHint=false, so the safety profile is covered. The description adds two useful facts beyond them: the return format (pairs as arrays) and a per-call cost of 1 credit, which is genuine value. It stops short of clarifying the 'unique' semantics when the input has repeated values, or what is returned when no pairs match.

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?

Three short sentences, front-loaded with the purpose, then return shape, then cost. No filler and nothing that could be trimmed without losing 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 two-parameter pure-computation tool with no output schema, the description reasonably covers behavior and return shape. Gaps remain around duplicate handling (the meaning of 'unique'), result ordering, and the empty-result case, which matter because callers cannot inspect an output schema to infer them.

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 the schema already documents both parameters, making the baseline 3. The description restates 'list of integers' and 'target value' but adds nothing about format details, and notably it does not reconcile the schema's odd typing where target is declared as a string and nums as an array of strings.

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 names a specific verb and outcome: it finds all unique integer pairs in a list summing to a target, and states the return shape (arrays of pairs). This is clearly distinguishable from generic siblings like calculate or average, though it doesn't explicitly contrast itself with near-neighbours such as find_duplicate_number or find_majority_element.

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?

Usage is only implied: given a list and a target, this returns matching pairs. There is no statement of when to prefer it over other algorithmic siblings, no preconditions, and no exclusions (e.g., what to do if no pairs exist or if the list contains duplicates).

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered by structured data. The description adds useful non-structured context: it returns an index rather than a value, and each call costs 1 credit. It does not discuss behavior on empty input, ties, duplicates, or the first/last element edge cases.

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?

Three short sentences, front-loaded with purpose, then return value, then cost. No filler; the credit note is genuinely useful. Slight redundancy in saying both 'finds' and 'returns the index'.

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 single-parameter algorithmic utility with no output schema, the description covers purpose, return type, and cost, which is most of what an agent needs. Missing only edge-case behavior (empty list, single element, duplicates) and input format expectations.

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%, so the schema already documents the single 'nums' parameter. The description only restates it as 'a list', which does not resolve the schema's actual ambiguity between an array of strings and a plain string input, nor the discrepancy between schema coverage and the zero-required-parameters signal.

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 a specific verb and resource ('Finds a peak element in a list') and defines the target precisely as an element greater than its neighbors, and adds that the return value is an index. An agent can distinguish it from generic maxima tools like find_max_subarray_sum, though no sibling is named explicitly.

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 instead of related array tools such as find_max_subarray_sum, find_majority_element, or binary_search on a rotated array. The phrase 'any peak element' hints that multiple valid answers exist, but no conditions, prerequisites, or alternatives are given.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is fully covered elsewhere. The description adds two pieces of real context beyond the annotations: the recursion is depth-unbounded, and each call consumes 1 credit. It does not discuss ordering of output elements, treatment of empty nested lists, or error 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?

A single front-loaded sentence describing the transformation, with the credit cost appended in parentheses. Nothing is redundant; the only mild weakness is that the cost note is bolted on rather than integrated.

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 one-parameter, non-destructive utility with no output schema, the description covers what the tool does and its cost. The remaining gap is the unexplained string-accepting variant of the input, which an agent would have to discover from the schema alone.

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 the baseline is 3, but the description actually under-serves the schema: the parameter accepts either an array of strings or a plain string (anyOf), and the description only describes a 'nested list structure', leaving the string form unexplained. That is a case where the description narrows rather than clarifies the parameter contract, so it falls below baseline.

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 a specific verb ('flattens') and resource ('nested list structure') and defines the result precisely as a single-level list produced by recursive extraction at any depth. The operation is unmistakable and cannot be confused with nearby list utilities such as merge_sorted_lists or sort_numbers, though no sibling is named explicitly.

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 when-to-use or when-not-to-use guidance, no prerequisites, and no named alternatives. Usage is only implied by the description of the transformation itself, which for a simple one-step utility is adequate but leaves the agent to infer context.

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

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

Annotations already declare readOnly, idempotent, non-destructive behavior, so the description's job is lighter; it still adds two genuinely useful behavioral facts the annotations do not carry: the normalization rules (case- and whitespace-insensitive) and a cost of 1 credit. It stops short of stating the return type (presumably a boolean), which is the only behavior an agent must still guess.

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?

A single sentence front-loads the operation, the exact algorithm, and the normalization rules, with the credit cost appended in parentheses. There is no filler or redundancy.

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

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

For a simple two-string predicate with full schema coverage and complete safety annotations, the definition covers purpose, semantics, normalization, and cost. The only missing piece an agent might want is the shape of the response (boolean vs. structured result), which no output schema supplies.

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 word1 and word2 are already documented as required strings with no units or defaults. The description adds only the comparison semantics ('ignoring case and whitespace'), which applies to both inputs — helpful but minimal beyond the schema, so the baseline 3 holds.

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 gives a specific verb and resource ('Checks if two words are anagrams') and then defines the exact comparison rule (same characters, same frequencies), so an agent can distinguish it from neighbors like is_palindrome or is_rotation without opening the schema.

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 purpose makes the applicable situation obvious, but there is no explicit when-to-use/when-not-to-use statement and no routing to or from sibling tools such as is_palindrome or text_stats. Usage is implied rather than stated.

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, so the safety profile is covered. The description adds genuinely new behavioral context: the operation costs 1 credit and the comparison rule ignores case, spaces, and non-alphanumerics, which affects what inputs will be judged true. It stops short of stating the return type, but that is minor here.

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

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

One sentence that front-loads the core purpose, then the qualifier on how matching works, followed by a short cost note. Nothing is redundant or buried.

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 one-parameter, fully documented utility with annotations already covering safety, the description is nearly complete. The only gap is that it never says what is returned (a boolean), though the name and annotations make that inferable.

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 is a single parameter with 100% schema description coverage, so the schema already does the heavy lifting and a baseline of 3 applies. The description adds a little meaning by implying that non-alphanumeric characters in the input are tolerated and ignored.

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 a specific verb and resource ("Checks if a string is a valid palindrome") and then pins down the exact matching semantics: alphanumeric only, case-insensitive, spaces ignored. That precision distinguishes it from nearby siblings such as is_anagram or reverse_string without ambiguity.

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 explains what counts as a palindrome but gives no guidance on when to pick this tool over alternatives (is_anagram, reverse_string) or when it is inappropriate. The normalization rules are semantics, not selection 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 declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds genuinely useful non-schema context in '(cost: 1 credit)', but says nothing about the return value (boolean?), behavior on negative inputs, or how a string argument like '16' is handled despite the parameter accepting strings.

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?

A single sentence with an inline clarifying parenthetical and the credit cost appended. Nothing is redundant and the core meaning is front-loaded.

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

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

For a one-parameter predicate with full annotation coverage and full schema coverage, the description is nearly complete; the only meaningful gap is that with no output schema it does not say the result is a true/false answer, nor how edge cases (0, negatives, non-integer strings) are treated.

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% and the single parameter is fully documented as 'number to check, no unit, no default'. The description's parenthetical adds conceptual meaning to 'perfect square' but no syntax or input-handling detail beyond the schema, so the baseline 3 applies.

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 a specific verb ('Checks') and a precise mathematical property, reinforced by the parenthetical definition 'the square of an integer'. It does not explicitly differentiate itself from adjacent predicate siblings such as is_prime or is_power, so it falls just short of 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?

Usage is only implied by the name and the word 'Checks'; there is no statement of when to prefer this over is_power, is_prime, or a general calculate call, and no exclusions or prerequisites are given.

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 that this is a safe read-only, idempotent operation. The description adds meaningful behavioral detail by defining the exact truth condition with non-negative integer exponents and by disclosing the one-credit cost.

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 definition is three short sentences, front-loads the core purpose, then gives the exact condition and cost. There is no redundant or filler wording.

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 mathematical predicate with full schema coverage, annotations, no output schema, and only two parameters, the description provides enough information to invoke it correctly. It defines the operation, return condition, and cost.

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%, so the input schema already defines num and base. The description adds value by making the relationship explicit: num is checked against base^k for non-negative integer k.

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 operation: checking whether a number is an exact integer power of a specified base, and it gives the exact condition num = base^k. This is clear, though it does not explicitly distinguish itself from the nearby specialized tool is_power_of_two.

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?

Usage is implied by the mathematical definition, so an agent can infer when to call it. However, the description gives no explicit when-to-use guidance, exclusions, or comparison with alternatives such as is_power_of_two or is_perfect_square.

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. The description adds the checking algorithm and a 1-credit cost, which is useful behavioral context beyond the annotations, though it does not state edge-case behavior such as case sensitivity or empty-string handling.

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, uses two tightly written sentences, and includes only relevant information: what is checked, how it is checked, the return value, and the cost.

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 two-parameter boolean predicate, the description covers the operation, return value, and cost, and the schema fully documents both inputs. It could be more complete by specifying edge-case semantics such as case sensitivity or empty-string treatment.

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 both parameters are already documented by the schema. The description clarifies the direction of the check by saying 'str1 appears in str2 concatenated with itself,' but it does not add syntax or format details beyond the schema baseline.

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: 'Checks if one string is a rotation of another string.' It also names the inputs and the boolean return, which distinguishes it clearly from sibling string predicates such as is_anagram and is_palindrome.

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 operation's purpose implies its usage, but the description gives no explicit when-to-use guidance, no exclusions, and no alternatives. For a simple predicate this is mostly adequate, but it leaves edge-case selection to inference.

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 readOnly, idempotent, non-destructive, open-world behavior, so the safety profile is covered. The description adds two genuinely useful facts beyond that — the boolean return value (no output schema exists) and the 1-credit cost — but says nothing about edge-case behavior such as how negative or very large numbers are handled.

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?

Three short sentences, front-loaded with the definition before the return contract and the cost note, with no filler. The parenthetical example is slightly redundant with the definition but earns its place by disambiguating the rule.

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 one-parameter boolean predicate with annotations and no output schema, the definition covers purpose, return value, and cost adequately. The only missing piece is what happens for invalid input (negatives), which is partly covered by the schema but not by the description.

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%, so the single 'num' parameter is already fully documented, including the 'must be non-negative' constraint. The description's example illustrates the concept but adds no syntax or format detail beyond the schema, so baseline 3 applies.

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 a specific predicate with its exact mathematical condition and an inline worked example (153 = 1³ + 5³ + 3³), which removes any ambiguity about what 'sum of cubes' means. An agent can trivially distinguish it from neighbors like is_prime, is_perfect_square, or is_power_of_two.

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 predicate nature implies usage clearly, but the description gives no explicit when-to-use or when-not-to-use guidance and never points to sibling tools (e.g., is_armstrong_number-style overlaps such as is_power or is_perfect_square) that an agent might confuse it with. Adequate but leaves routing to inference.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds real behavioral context beyond that: the precise IPv4 acceptance rule (four octets, 0-255) and a per-call cost of 1 credit. It still does not say what is returned (a boolean) or how IPv6 input is handled.

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?

A single front-loaded sentence states the operation and its acceptance criteria, with the credit cost appended as a compact parenthetical. Nothing is wasted and no re-reading is required.

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 one-parameter predicate with no output schema and full annotation coverage, the description supplies everything needed to call it correctly. The remaining gap is the return value contract (presumably a boolean), which the description leaves implicit.

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% and there is a single, self-explanatory 'ip' parameter, so the schema carries the parameter documentation burden. The description reinforces the expected format (dotted-quad IPv4) but adds no syntax examples or edge-case rules (leading zeros, whitespace) beyond what the schema 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?

States a specific verb ('Validates') and resource ('a string ... IPv4 address') and pins down the exact rule set: four dot-separated octets in the 0-255 range. An agent can tell this apart from lookup-oriented siblings like ip_lookup. It stops short of naming those siblings explicitly, so differentiation is inferred rather than stated.

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

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

Usage is implied by the word 'validate' plus the IPv4 constraint, but there is no explicit when-to-use / when-not-to-use guidance and no mention of the closest alternative, ip_lookup, which would be the wrong choice for pure format checking. An agent must infer that this tool does not resolve, geolocate, or accept IPv6.

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

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

Annotations already declare the tool as read-only, idempotent, non-destructive, and open-world, so the safety profile is covered. The description adds useful behavioral detail by specifying the exact return condition and the one-credit cost, though it does not address edge cases such as empty strings or non-bracket characters.

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

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

The description is two short sentences plus a cost parenthetical. The validation condition is front-loaded and every sentence contributes directly to understanding the tool.

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

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

For a simple one-parameter validator with full schema coverage and no output schema, the description is largely complete because it defines the return condition. Minor edge-case behavior is omitted, but an agent has enough information to call the tool correctly.

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

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

Schema description coverage is 100%, so the single string parameter is already fully documented in the input schema. The description adds no parameter-specific format or constraint details beyond calling it a string, which meets the baseline for high schema coverage.

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

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

The description states a specific verb, 'checks,' and a precisely scoped resource: balanced and nested parentheses, brackets, and braces. It also defines the true condition, making it clearly distinguishable from siblings such as is_palindrome or json_validate.

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 clearly by the name and description, but there is no explicit guidance on when to choose this tool over alternatives like json_validate or validate_sudoku. No when-not-to-use conditions are given.

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 readOnly, idempotent, non-destructive, and open-world behavior. The description adds important behavioral detail beyond those annotations: dict2 values overwrite dict1 values for duplicate keys, and the call costs 1 credit.

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 definition is one tightly written sentence plus a brief cost note. It is front-loaded with the operation and key semantics, with no unnecessary wording.

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 dictionary utility with full schema coverage and annotations covering safety, the description is nearly complete. It explains the merge outcome and duplicate-key behavior, though it does not describe input formatting or explicitly state the return shape.

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 the baseline is 3. The description improves on the schema by specifying merge precedence: values from the second dictionary overwrite those from the first for duplicate keys, which is not captured in the parameter descriptions.

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 ('Merges two dictionaries') and defines the resulting merge behavior for duplicate keys. It distinguishes itself from list-merge siblings such as merge_sorted_lists by explicitly naming dictionaries and value precedence.

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 operation and overwrite semantics are clear, so the use case is implied. However, the description does not explicitly state when to choose this tool over alternatives or mention any exclusions or prerequisites.

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

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

Annotations already declare readOnly, idempotent and non-destructive, so the safety profile is covered. The description adds the ascending-order guarantee and, usefully, a 'cost: 1 credit' note not present in annotations. It does not say what happens if inputs are unsorted or how the string form of a list is interpreted.

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?

Three short sentences, front-loaded with purpose, then return value, then cost. Every sentence carries information and nothing is padded.

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 pure-list transformation with only two parameters, this is nearly sufficient: purpose, ordering guarantee, return value and cost are all stated, and no output schema is needed. The only gap is undefined behavior on unsorted input or on the string parameter variant.

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 is 3. The description reiterates that both inputs are sorted lists but adds nothing about the two accepted shapes (array of strings or a bare string) revealed by the schema, leaving that ambiguity untouched.

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 a specific verb and resource ('Merges two sorted lists into a single sorted list') plus the resulting ordering, which is unmistakable against siblings like sort_numbers, flatten_list or merge_dictionaries. It stops short of explicitly naming any sibling, so a 4 rather than 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?

Usage is implied only: the caller learns the inputs must already be sorted, which functions as a precondition, but there is no statement of when to prefer this over a general sort or another merge helper, and no exclusion cases.

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

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so safety is covered without the description. The description adds two useful behavioral facts: the algorithm used (interval scheduling) and a per-call cost of 1 credit, which annotations cannot convey; however it says nothing about the shape of the result or error behavior for malformed intervals.

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 short sentences, front-loaded with the purpose, followed by the algorithm and cost note. Nothing is padded, though the algorithm sentence is arguably optional garnish.

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 single-parameter, no-output-schema computation tool this covers the essentials (what it computes and honest naming), but it stops short of stating that the return value is a single integer count and does not clarify the interval input encoding that the schema leaves 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 100%, so the baseline is 3. The description restates that intervals are supplied but adds no format detail, and it does not resolve the schema's ambiguity where the parameter is typed as an array of strings or a plain string while the schema text describes an array of arrays.

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 gives a specific verb and resource ('Finds the minimum number of meeting rooms required') plus the computation's premise ('given their time intervals'), which is unambiguous. It does not differentiate from the nearby sibling can_attend_meetings, so an agent must infer the boundary itself.

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?

Usage is implied by the problem framing (supply meeting intervals, get the minimum room count), but there is no explicit when-to-use versus when-not, no mention of can_attend_meetings as an alternative, and no prerequisite guidance. Adequate but leaves the routing decision to the agent.

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 establish that this is read-only, idempotent, and non-destructive. The description adds useful behavioral context by disclosing the 1-credit cost and clarifying the returned value, though it does not discuss edge cases or error 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 description is front-loaded and very short: one clear sentence plus a parenthetical cost note. Every part earns its place, and the return semantics are clarified without unnecessary wording.

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 pure utility with full schema coverage and rich annotations, the description supplies the operation, the result, and the cost. No output schema is needed because the return value is explicitly stated.

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 single text parameter is fully documented in the schema as required text to reverse. The description adds no syntax, format, or constraint details beyond what the schema already provides, so the baseline score for 100% schema coverage 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 names a precise verb and resource: it reverses the order of characters in a given string. It also states the return result, so the operation is unambiguous and distinguishable from sibling utilities.

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 use case is implied by the operation itself: call it when a string needs to be reversed. However, there is no explicit when-to-use or when-not guidance, nor any comparison to related sibling tools such as text_stats or is_palindrome.

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, destructiveHint=false and openWorldHint, so the safety profile is covered by structured data. The description adds genuinely useful non-schema context — that the algorithm is deterministic and that each call costs 1 credit — but says nothing about return shape or handling of non-numeric input.

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 short sentences, front-loaded with the operation and its result order, with the cost note appended cleanly. "using a deterministic sorting algorithm" is mild filler but does carry the idempotence/determinism signal, so it roughly 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 trivial single-parameter read-only utility with full schema coverage and annotations covering safety and idempotence, the description supplies everything needed to call it correctly. Only the input-format ambiguity and return format are unaddressed, and no output schema exists to offset that.

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% and the single parameter is fully documented in the schema, so the baseline is 3. The description reinforces that the input is numeric values to be ordered ascending, but does not clarify the anyOf ambiguity (array of strings vs. a single string) that the schema leaves open.

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 ("Sorts a list of numbers") plus the resulting order (ascending), so the operation is unambiguous. It does not distinguish itself from adjacent list/ordering tools such as merge_sorted_lists or find_n_largest, which is the only thing keeping 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?

Usage is only implied by the noun phrase "list of numbers"; there is no explicit statement of when to prefer this over sibling tools like merge_sorted_lists or find_n_largest, and no preconditions are given. Adequate for an obviously-scoped utility, but no routing 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 declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds useful behavior beyond those annotations by stating the boolean return value and the one-credit cost, though it does not describe handling of malformed boards or the string representation allowed by the schema.

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 and compact: purpose, validation rules, return behavior, and cost appear in three short sentences. There is no unnecessary 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 simple one-parameter validation tool with rich annotations and full schema coverage, the definition is nearly complete. The main remaining gap is that malformed-board handling and the exact string board format are not clarified, but the boolean return and cost are covered.

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 the single board parameter is already documented in the schema. The description only repeats that the input is a 9x9 board and does not clarify the schema's anyOf choice between a 2D string array and a string representation, so it adds little parameter meaning 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 uses a specific verb and resource: checks whether a 9x9 Sudoku board is valid. It also names the exact validation rules—rows, columns, and 3x3 sub-grids must contain no duplicate digits 1-9—so the agent can distinguish this from generic calculation or puzzle tools.

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 usage is implied by the name and the validation rules, but there is no explicit when-to-use or when-not-to-use guidance. No alternatives or prerequisites are named, which is acceptable for a narrow utility but leaves usage context implicit.

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