Orb Platform

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

No annotations provided, so description carries full disclosure burden. While it lists what is inspected, it fails to state what the tool returns (structured report? score? flagged spans?), whether it modifies input text, or how it handles sensitive PII data it is configured to detect. Missing safety/permission context for a compliance tool processing potentially regulated data.

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

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

Single dense sentence with zero filler. Front-loaded verb and parallel list structure makes scannable. Could improve by adding a second sentence for output format without sacrificing clarity.

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

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

Tool has nested object complexity and compliance domain implications, yet lacks output schema. Description does not compensate by explaining return structure, success/failure states, or data handling guarantees necessary for a PII-scanning tool. Insufficient for safe invocation without trial-and-error.

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 documented ('Text to check' and the policy object structure). Description adds context that 'brand voice violations' connects to the policy parameter, but baseline 3 applies since schema already defines all fields comprehensively.

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?

Lists specific verification targets (PII, profanity, Flesch-Kincaid, GDPR/COPPA) and uses a clear action verb 'Check'. Distinguishes clearly from sibling educational tools (lookup_word, get_lesson, etc.). Deducted one point because 'Check' is ambiguous regarding output (returns boolean? report? redacted text?) and the tool's title is null, forcing the description to carry full identification weight.

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

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

Provides no guidance on when to prefer this over generate_text (which might produce content needing validation) or how it relates to the educational content tools in the sibling set. No prerequisites or exclusion criteria listed.

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?

Without annotations, the description carries the full disclosure burden. It provides valuable behavioral context by revealing data sources (dictionary for known words, AI for unknown), which implies latency/cost tradeoffs. However, it omits safety classification (read-only vs. destructive), return format details, and error handling 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 consists of two efficient sentences with zero waste. It is front-loaded with the core action ('Get an age-calibrated definition') followed by implementation details, making it immediately scannable.

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 2-parameter tool with no output schema and no annotations, the description adequately covers the core value proposition (age calibration, AI fallback). However, it lacks necessary operational context such as safety profile, rate limiting, or return value structure that would typically be provided by annotations or a more detailed 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 description coverage is 100%, establishing a baseline of 3. The description adds semantic context by referencing 'age-calibrated' (mapping to the age_group parameter) and 'term', but does not elaborate on parameter syntax, validation rules, or the default 'adult' value beyond what the schema already documents.

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

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

The description states a specific verb ('Get') and resource ('definition') with a clear differentiator ('age-calibrated'). It distinguishes itself from sibling lookup tools by specifying the dual-source approach (dictionary data vs. AI), though it could explicitly contrast with 'lookup_word'.

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

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

The description provides no explicit guidance on when to use this tool versus alternatives like 'lookup_word' or 'search_words'. It does not specify prerequisites, exclusions, or selection criteria beyond the implicit age-calibration feature.

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

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

With no annotations, description carries full burden. It successfully discloses the graph traversal mechanism ('knowledge graph'), but omits read-only confirmation, traversal depth limits, or performance characteristics typical for graph exploration tools.

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

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

Single efficient sentence (11 words). Front-loaded with action verb, immediately identifies input and output scope with zero redundancy or filler.

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

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

Adequate for a 1-parameter exploration tool. Compensates for missing output schema by enumerating return categories (lessons, themes, words). Minor gap on pagination/result limits or empty result handling.

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

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

Schema coverage is 100% (1/1 parameters described). Description mentions 'Trace a word' which aligns with the 'word' parameter, but adds no syntax examples, constraints, or semantic notes beyond the schema's 'Word to trace through the graph'.

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?

Excellent specificity: verb 'Trace' + resource 'knowledge graph' + concrete outputs ('lessons, themes, and related words'). Distinguishes from sibling get_related_words (just words) by including pedagogical content like lessons and themes.

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

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

Provides implied usage through output description (use when seeking connected lessons/themes), but lacks explicit when/when-not guidance vs alternatives like get_related_words or get_lesson.

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?

Mentions 'policy enforcement' which hints at safety constraints, but with zero annotations provided, the description carries the full burden of behavioral disclosure. Missing critical context: whether 'retrieve' fetches external data, if 'negotiate' maintains conversation state, persistence guarantees, or side effects.

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

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

Single sentence is efficiently constructed and front-loaded, though the dense list of seven verbs makes it hard to scan. No redundant fluff, but appropriate given the complexity.

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

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

With seven distinct operating modes, no output schema, and no parameter documentation, the description is insufficient. No explanation of return formats, error behaviors, or the scope of 'policy enforcement' relative to the various tasks.

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

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

Schema coverage is 0% - both parameters lack descriptions. The description lists the enum values for 'task' but provides no guidance on what the 'input' parameter should contain for each mode (e.g., format for 'negotiate' vs 'compliance_check', expected language codes for 'translate'). Fails to compensate for the schema gap.

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

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

Lists seven disparate verbs (write, summarize, retrieve, negotiate, etc.) but fails to establish a coherent scope or resource model. Doesn't clarify why these functions are grouped together or what differentiates this from sibling tools like `translate_word`, `define_term`, or `check_compliance` which appear to handle specific subsets of these tasks.

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

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

Provides no guidance on when to use this multi-purpose tool versus the specialized siblings (e.g., `translate_word` vs. the 'translate' task here). No mention of prerequisites, input requirements per task, or when this tool is preferred over alternatives.

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

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

With no annotations provided, the description carries the full burden. It explains what lesson content looks like (5 phases, tracks, ages) but omits safety/read-only status, error handling behavior, or whether the lesson is cached/generated. The '365 days' implies temporal bounds which helps.

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

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

Extremely compact with zero redundancy. Two dense phrases convey the structure and parameter matrix. Slightly telegraphic (lacks articles/verbs) but efficient. Could benefit from one sentence explaining the retrieval action for absolute clarity.

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

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

Given no output schema and no annotations, the description adequately covers the parameter meanings through the dimensional matrix (365/4/3) and explains the lesson format. Missing explicit description of return structure or error states, which would be valuable for a content retrieval tool.

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

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

Excellent compensation for 0% schema coverage. The phrase '365 days x 4 tracks x 3 age groups' maps perfectly to the three parameters (day, track, age) and explains their domains (day 1-365, four track enum values, three age enum values) without repeating the schema structure.

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 effectively explains the content model (5-phase lesson structure) and distinguishes this from siblings like get_quiz or word_of_the_day by specifying the unique pedagogical format. However, it relies on the tool name 'get_lesson' to imply the retrieval action rather than stating it 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?

Provides no guidance on when to use this tool versus siblings (e.g., when to use get_quiz vs get_lesson) or prerequisites. The description is purely descriptive of the content structure rather than prescriptive of usage contexts.

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

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

No annotations provided, so description carries full burden. It discloses output characteristics ('6 question types', formative purpose) but omits operational traits: idempotency, cache behavior, auth requirements, or whether questions are deterministic/randomized.

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

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

Two efficient sentences with zero waste. Front-loaded with core resource identification ('Assessment questions'), followed by distinguishing attributes ('6 question types', 'formative evaluation').

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

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

Given 0% schema coverage and no annotations, the description inadequately documents the two parameters. While output concept is clear (questions), input semantics are missing, leaving agents uncertain how to specify day/track values.

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

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

Schema has 0% description coverage. Description mentions 'lesson content' implying params identify lessons, but fails to explain 'day' (calendar day? sequence number?) or 'track' (learning path? difficulty level?) semantics or valid values.

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

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

The description clearly identifies the resource ('Assessment questions') and source ('from lesson content'), and distinguishes from sibling get_lesson by specifying quiz/assessment focus. Lacks an explicit action verb (retrieve/generate), preventing 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?

No explicit guidance on when to use versus alternatives (e.g., get_lesson for content vs. assessment). 'Formative evaluation' implies pedagogical use case but does not state prerequisites or exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively discloses the scope of returned data (four specific relation types) but omits safety characteristics (read-only status), volume expectations, rate limits, or error handling behavior for unknown words.

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

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

The description is a single, efficient sentence with zero redundant text. It front-loads the action verb and immediately enumerates the specific relation types, placing the most critical information first without filler.

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

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

Given the low complexity (single string parameter, no nested objects, no output schema), the description adequately covers the tool's functionality by specifying the four relation types returned. While it could benefit from describing the return structure, the semantic content of the output is sufficiently explained for a simple lookup tool.

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

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

The input schema has 100% description coverage for its single parameter ('English word to find relations for'). The description adds minimal semantic value beyond the schema, merely reinforcing that it accepts 'any word'. With complete schema coverage, this baseline adequacy is appropriate.

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

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

The description uses a specific verb ('Find') and enumerates exact relation types sought (semantic neighbors, rhymes, same-root words, knowledge graph connections), providing clear scope. However, it does not explicitly differentiate from the sibling tool 'explore_graph', which also deals with graph connections, creating potential selection 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?

No explicit when-to-use or when-not-to-use guidance is provided, nor are alternatives named. However, the specific categories of relations listed (rhymes, same-root words) provide implicit usage context, signaling this tool is appropriate for linguistic and semantic association tasks rather than simple definitions.

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

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

With no annotations provided, description carries full burden. It valuably discloses that images are categorized by universality, culture, and age, but omits return format (URLs vs base64), read-only safety confirmation, error handling for unknown words, or rate limits.

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

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

Single efficient sentence with zero waste. Main action front-loaded ('Get all available images'), followed by hyphenated clarification of scope. Every phrase earns its place by distinguishing the content variants.

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

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

Adequate for a single-parameter retrieval tool. The variant categorization hints at return structure, but given no output schema exists, description should ideally specify return format (array of objects, URLs, etc.) and whether results are empty when word not found.

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

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

Schema coverage is 100% with the single 'word' parameter fully described. Description focuses on operation scope rather than adding parameter syntax details, which is appropriate given schema completeness. Baseline 3 justified.

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

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

Specific verb 'Get' with clear resource 'images' and explicit scope 'all available' including distinct variant types (universal, culture-specific, age-specific). Clearly distinguishes from text-focused siblings like define_term, lookup_word, and translate_word.

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

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

Implies usage context through the variant categorization (culture/age-specific), but lacks explicit when-to-use guidance or mention of prerequisites like 'use when visual representation needed' or 'check lookup_word for definitions first'.

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

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

With no annotations provided, the description must carry full behavioral disclosure. It successfully reveals the output structure and scope (47 languages), but omits critical behavioral traits like error handling when words aren't found, rate limits, case sensitivity rules, or whether multi-word phrases are supported.

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 single-sentence structure with an em-dash is appropriately front-loaded (action first) and information-dense. The phrase 'for verified agent output' slightly obscures clarity (unclear if modifying 'languages' or the entire output), preventing a perfect score.

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

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

Given the tool's low complexity (1 parameter, no nested objects) and absence of an output schema, the description adequately compensates by detailing the four specific data types returned (IPA, definition, etymology, translations). It provides sufficient context for an agent to understand the tool's utility despite missing error-handling documentation.

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

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

With 100% schema description coverage for the single 'word' parameter, the schema already documents the input sufficiently. The description reinforces this with 'any English word' but adds minimal semantic depth beyond the schema's 'English word' description, meeting the baseline expectation for high-coverage schemas.

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

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

The description provides a specific action ('Look up') with a clear resource ('English word'), and distinguishes itself from siblings like 'translate_word' and 'define_term' by explicitly listing its comprehensive return data (IPA, etymology, translations across 47 languages), clearly positioning it as the full-featured lexical lookup option.

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

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

While the description doesn't explicitly state 'when to use vs alternatives,' it implicitly guides selection by detailing the specific output fields (IPA, etymology, etc.), suggesting this tool should be chosen when comprehensive linguistic data is needed rather than just definitions or translations. However, it lacks explicit exclusions or direct 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?

With no annotations provided, the description carries the full burden and successfully discloses key behavioral traits: results are 'ranked by semantic similarity' and the search covers '162,000 English words.' This reveals the approximate/fuzzy matching nature and corpus scope. Could improve by mentioning read-only safety or empty-result 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?

Three sentences with zero waste: sentence 1 states purpose, sentence 2 provides usage guidance, sentence 3 discloses return behavior and dataset scope. Information is front-loaded with the core action, and every sentence earns its place.

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

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

For a simple 2-parameter tool with no output schema, the description is appropriately complete. It explains the return value structure conceptually ('words ranked by semantic similarity') even without formal output schema documentation. No critical gaps given the tool's simplicity.

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

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

Schema description coverage is 100%, establishing a baseline of 3. The description implicitly references the query parameter ('description of its meaning') but does not add semantic details beyond the schema, such as example query formats or guidance on the limit parameter. The schema adequately 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?

The description states a specific verb ('Find'), resource ('English word'), and mechanism ('given a description of its meaning'). It clearly distinguishes from siblings like lookup_word (which assumes knowing the word) and define_term (which provides definitions for known words) by specifying the reverse-direction lookup.

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

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

Provides explicit positive guidance ('Use when the user describes a concept but doesn't know the word') that clearly signals the appropriate context. Lacks explicit negative guidance or named alternatives, but the when-to-use statement is specific enough to prevent misuse.

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

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

No annotations provided, so description carries full burden. Explains search matching behaviors (prefix, fuzzy, definition content) which is helpful. However, missing safety disclosure (read-only status), return value structure, rate limits, or pagination behavior. Adequate but not comprehensive given zero annotation coverage.

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, zero waste. First sentence establishes search modes, second clarifies target (pattern or meaning). Every word earns its place with no redundancy or tautology.

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

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

Adequate for a 4-parameter search tool with simple schema types. Covers input semantics well. However, lacks output description (no output schema exists) and safety annotations, leaving gaps in the complete operational picture.

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

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

Schema coverage is 100%, establishing baseline 3. Description adds value by mapping abstract search concepts ('prefix', 'fuzzy match', 'definition content') to the parameter combinations (e.g., implying field='word' enables prefix matching, fuzzy=true enables fuzzy logic, field='definition' searches content). This semantic context aids parameter utilization beyond raw schema definitions.

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

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

Clear verb ('Search') and resource ('dictionary'). Distinguishes from exact lookup tools (like 'lookup_word' implied by sibling list) by specifying three search modes: prefix, fuzzy match, and definition content. However, it does not explicitly reference siblings to clarify when to select this over alternatives.

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

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

Describes capabilities (prefix, fuzzy, definition search) but provides no explicit when-to-use guidance or named alternatives. Lacks indication of prerequisites or when NOT to use this tool versus 'lookup_word' or 'get_related_words'.

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

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

With no annotations, the description carries the full burden. It successfully discloses the dual backend behavior (47-language dictionary vs AI) and performance characteristics ('instant'), but omits output format, error handling, authentication requirements, and rate limits.

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

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

Three efficient sentences with zero waste. Front-loaded with the core purpose (translate), followed by mechanism specifics. Every clause provides distinct value (scope, speed, method).

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

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

Adequate for a simple two-parameter tool, but missing output format disclosure since no output schema exists. Given the mutation-like nature (AI translation for sentences), additional context on data handling or privacy would elevate this.

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

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

Schema coverage is 100%, establishing a baseline of 3. The description implies the 'input' parameter accepts both words and sentences, and '47-language' hints at 'target_lang' capabilities, but adds no syntax, format constraints, or examples beyond the schema.

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

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

Clear verb (Translate) and resource (word or phrase). The dual-mechanism disclosure (dictionary vs AI) adds specificity that distinguishes it from generic translation tools. However, it doesn't explicitly differentiate from sibling 'lookup_word' or 'define_term' 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?

Describes the internal routing (dictionary for words, AI for sentences) but provides no guidance on when to choose this tool over siblings like 'define_term' or 'lookup_word', nor does it mention prerequisites like supported language codes.

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

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

With no annotations provided, the description carries the full disclosure burden. It successfully reveals the deterministic nature and authentication requirements, but omits return format details, error behaviors, or rate limits that would help the agent handle responses.

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 sentences with zero waste. Front-loaded with the action ('Get'), followed by behavioral constraints ('Same word', 'No auth'). Every sentence earns its place with high information density.

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

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

For a tool with low complexity (1 optional parameter, simple concept) and 100% schema coverage, the description covers the essential behavioral context (determinism, auth). Lacks explicit return value description, but 'word of the day' provides sufficient implication given the domain.

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

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

Schema coverage is 100% with the date parameter fully documented in the schema. The description implies the date parameter through 'on a given date' but adds no semantic detail beyond the schema's ISO format description. Baseline 3 appropriate 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 uses a specific verb ('Get') with a clear resource ('word of the day'). The phrase 'deterministic' and 'Same word for all users' effectively distinguishes this from siblings like generate_text or lookup_word, clarifying this retrieves a fixed daily value rather than generating or searching.

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 'No auth required' statement provides implicit usage guidance (use when authentication is unavailable), and 'Same word for all users' clarifies the shared nature. However, it lacks explicit when-to-use comparisons against siblings like lookup_word or get_lesson.

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