motionprompts

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

With no annotations, the description carries the full burden of behavioral disclosure. It thoroughly explains what each include payload contains ('meta' full record, 'preview' thumbnail and demo URL, 'prompt' self-contained brief, 'source' actual files plus integration note) and the default combination. This goes beyond a generic fetch and gives the agent a concrete model of the tool's 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 four sentences, front-loaded with the core purpose, and every sentence adds value. It avoids fluff and repetition, making it efficient for an agent to parse.

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 absence of an output schema and annotations, the description does a good job of explaining the return payloads and the 'include' options. It lacks details on edge cases like invalid slugs or error handling, but for a fetch-by-slug tool with moderate complexity, the description covers the essential behavior.

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?

While the schema already describes both parameters, the description adds substantial meaning by detailing the enum values for 'include' and providing the default. For example, it explains that 'prompt' is a self-contained brief to rebuild faithfully and 'source' includes actual files. This enriches the agent's understanding beyond the schema's terse 'Which payloads to return.'

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 starts with 'Fetch one component by slug,' which is a specific verb+resource that clearly states the tool's function. It distinguishes this from sibling tools that list or search components, making the purpose unambiguous.

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

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

The description provides clear context on the 'include' parameter and explicitly advises to add 'source' when the real code is needed. It implies that this tool is for retrieving a known component, but it doesn't explicitly contrast with alternatives like search_components or list_components. No explicit exclusions, but enough guidance for typical use.

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

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

With no annotations provided, the description carries full burden. It discloses the exact output structure (three blocks), the fact that it returns an instruction to adapt the prompt manually, and specifies the arithmetic preservation rule (recompute chain/sum/ScrollTrigger end). It also states what the replaced tool did wrong, offering deep behavioral context.

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 dense but well-organized, with each sentence contributing a distinct purpose (proclamation, output structure, replacement rationale, reference). It is longer than ideal but earns its length through the critical arithmetic detail and forward references.

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

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

The description covers the tool's purpose, output composition, usage recommendation, and subtle calculation instruction, which is robust for a tool with no output schema. It lacks an explicit enumeration of return format fields or error conditions, but the provided information is sufficiently complete for selection and basic 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?

The description adds some context for motion_system via 'requested motion system', but the required parameter 'slug' is completely unexplained in both the schema and description. Since schema coverage is only 50%, the description should compensate, but it does not address the required parameter at all, leaving a significant 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?

The description uses a specific verb+resource ('get a build prompt') and clearly states it returns three blocks, defining the tool's scope. It explicitly distinguishes itself from sibling render_prompt by name and by explaining the difference, making the purpose unambiguous.

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 proclaims 'THE way to get a build prompt' and explicitly states it 'Replaces render_prompt', offering a direct when/why to use this tool over a named alternative. It even provides a rationale (render_prompt substituted mechanically and produced contradictory prompts), giving clear usage 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?

With no annotations, the description carries the full burden. It discloses the tool's behavior in detail: it returns rule violations (hard vs warnings), measured budget dimensions, and an ordered init plan; also notes that every step cites evidence. This is transparent for a read-only analysis tool, though it omits potential side effects like authentication 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?

The description is two sentences, front-loaded with the core purpose, and uses an em-dash list to succinctly enumerate key outputs and plan order. No filler words; every clause adds value.

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?

Without an output schema, the description thoroughly covers return values (violations, warnings, budget, init plan) and even details plan order. However, the input side is incomplete: optional parameters motion_system and moving_sections are not explained, so the agent may not invoke the tool correctly with all parameters.

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 only 33% (only moving_sections has a description). The tool description explains 'slugs' (components for a page) but provides no meaning for 'motion_system' or 'moving_sections', leaving agents uncertain about how to fill these optional parameters. The Spanish description for moving_sections adds limited clarity.

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

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

The description clearly states a specific action (returns) and resource (integration contract) with a defined scope: given slugs for one page. It lists concrete outputs (violations, warnings, budget, init plan) that distinguish it from sibling tools like get_component or suggest_mechanics.

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 a clear usage context: 'Given the slugs you intend to put on ONE page' indicates when to use the tool. It does not explicitly state when not to use it or name alternatives, so it lacks exclusions but provides a clear situational trigger.

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

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

No annotations are present, so the description carries the full burden. It discloses the return content ('full token set' plus 'native components') but does not explicitly state that the operation is read-only, describe any side effects, or explain what a 'token set' means. The term 'full' suggests complete data, but the behavior beyond that is opaque.

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

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

The description is a single short sentence (fragment) that is efficient and gets to the point. It is front-loaded with the core outcome ('full token set'). However, it lacks a verb and relies on the title for the action, making it slightly less self-contained, but it earns its place with no filler.

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

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

Given the simplicity (one parameter, no output schema, no annotations), the description provides the essential return information. Yet it leaves ambiguity around 'token set' and 'native components,' which are not defined. The absence of an output schema makes this a gap, though the low complexity keeps the overall completeness barely adequate.

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 one parameter ('name') with a clear description ('System name, e.g. scrub-lagged.'), covering 100% of parameters. The tool description adds no additional meaning for this parameter. With high schema coverage, the baseline is 3, and there is no extra value from the description.

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 title clearly states 'Get one motion system with its members,' which provides the verb and resource. The description adds specificity: 'Full token set of a motion system plus the components that are native to it.' This distinguishes it from list_motion_systems (plural) and get_component (single component), though it doesn't explicitly name these siblings.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or criteria for choosing it over sibling tools like search_components_motion or get_component. The only signal is the word 'one,' which implies single-item retrieval, but this is not actionable 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?

With no annotations, the description discloses the read-only nature via 'browse' and specifies the return format as a compact list of listed fields. It doesn't elaborate on pagination defaults, but the schema covers that, and the description adds useful context about output shape.

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

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

The description is two sentences, front-loaded with the core action and output fields, then a crisp usage guideline. Every phrase adds value with no 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 list tool with two optional pagination parameters and no output schema, the description covers the purpose, the specific fields returned, and the usage context. It is complete and self-sufficient.

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 limit and offset are already fully documented. The description does not add further parameter-specific meaning, so baseline 3 is appropriate.

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

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

The description clearly states the tool browses the whole catalog as a compact list and enumerates the exact fields returned. It also distinguishes itself from search_components by noting the difference in use cases.

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?

Explicitly states when to use this tool ('overview or when a brief is vague') and when to use an alternative ('use search_components when you have a specific brief'). This is strong guidance with a named alternative.

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 clearly indicates a read operation ('Return') and describes the output (filter vocabulary with counts). It also behaviorally frames the tool as a lookup/priming step, which is valuable context.

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 action and content. Every word earns its place; no filler or repetition.

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

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

The description is complete given the tool's simplicity: no params, no output schema. It explains both the returned data and the intended workflow context, leaving no ambiguity for 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?

The tool has zero parameters, so the description needs no parameter explanations. Per the rubric, a baseline of 4 applies for 0 params, and the description still adds value by explaining the output 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 clearly states the tool returns the filter vocabulary (mood, useCase, level, perfCost, category, deps) with counts, using a specific verb ('Return') and resource. It distinguishes itself from siblings like search_components by explicitly mentioning what it contributes to that workflow.

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

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

The description gives explicit when-to-use guidance: 'Call this first to learn the exact values search_components accepts.' This tells the agent to invoke this tool before search_components, implying it is a prerequisite and alternative usage is clearly scoped.

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 full transparency burden. It discloses that systems are derived from measured inventory, not invented, and specifies the exact count (8). Unlike a mutation tool, no side effects are relevant, but it could add detail about return structure or pagination; however, for a simple list, this is adequate.

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 concise sentences with no fluff. The first sentence front-loads the core purpose and data origin; the second provides actionable usage guidance. Every phrase earns its place, maintaining brevity while delivering essential information.

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

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

For a zero-parameter list tool with no output schema, the description is fully complete: it states what is listed, the provenance of the data, and the precise workflow for use. No output format is necessary since the inventory is known (8 items). The tool is simple, and this description covers all needed context.

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

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

The tool has zero parameters and an empty schema, so the baseline is 4. The description compensates by explaining the purpose and how to use the output, even though there are no parameters to document. It adds value by framing the list as a selection step for page composition.

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

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

The description explicitly states the tool lists the 8 motion systems of the library, identifying the resource (motion systems) and the action (list). It distinguishes from sibling 'get_motion_system' by emphasizing that this is the complete set and should be used first. The mention of token sets and measured inventory adds specificity.

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 clear when-to-use guidance: 'Use this FIRST when composing a page' and instructs to pick one system to render components coherently. This implies alternatives like get_motion_system for deeper details and indicates the tool's role in the workflow.

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

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

No annotations are provided, so the description carries the full burden, and it does so well: it explicitly states no images are generated, no API keys are used, and no network calls are made. It also discloses the shape of what it returns (per-model routing, transparency recipe, image-to-image retouch, six-crop sheets, Wikimedia logo sourcing).

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 core purpose is front-loaded and the sequencing constraint is last, which is the right priority order. It is somewhat dense with explanatory narrative ('lo que sigue delatando una página generada...'), but each clause carries concrete content (model routing, transparency, logos) rather than 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?

There is no output schema, so the description must convey the return content, and it enumerates the deliverables precisely (routing, transparency recipe, retouch recipe, crop sheets, logo sourcing). Combined with the stated prerequisites, an agent has everything needed to call it correctly.

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

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

Schema description coverage is 75%, so the schema already documents most parameters (brief, palette, sections, archetype). The description itself adds no parameter-level syntax or format detail beyond what the schema provides, so the baseline of 3 is appropriate.

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

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

States a specific verb and resource: it returns what image each section needs, which model to route it to, and how to write the prompt. It is clearly distinguished from plan_page (input) and render_prompt (execution) by positioning itself as the image-direction layer.

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?

Explicit sequencing: 'Llámala DESPUÉS de plan_page y ANTES de escribir un solo prompt de imagen.' It also states what the tool does NOT do (no image generation, no keys, no network), removing ambiguity about when it applies.

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 discloses key behavioral traits: the fixed 10-14 section count, the deliberate omission of components and the reasoning, the inclusion of a motion system and tokens, and the critical constraint that the reference biases but never derives motion. It also explains the downstream workflow. This is thorough and non-contradictory.

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 long but front-loaded with the primary output and then provides necessary exclusions, reasoning, and parameter caveats. Every sentence carries information, and the density is justified by the tool's complexity. A slightly tighter structure could group the reference behavior, but it remains readable and efficient.

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 absence of an output schema and annotations, the description does an excellent job of conveying what the tool returns (sections with roles, content needs, motion system), what it intentionally omits (components), and how to proceed afterward. It also covers the optional reference's role. This is complete enough for an agent to invoke correctly.

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

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

Schema coverage is 67%, and the description adds substantial meaning to the `reference` parameter (static art direction, biasing motion, not deriving motion), which the schema only lists as an object without explaining its effect. The `brief` and `archetype` are partially covered by schema and enum; the description adds no new detail for archetype but doesn't need to. Overall, it compensates for the coverage 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?

The description opens with a specific verb-resource pairing ('Returns the SECTIONS a page of this kind needs') and clearly distinguishes this planning action from component lookups by explicitly stating it returns NO components. It also names the companion tool (suggest_mechanics) and clarifies the motion system output, making the tool's purpose unmistakable among siblings.

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 explicit sequencing guidance: 'Ask for mechanics afterwards, one section at a time, with suggest_mechanics.' It also tells the agent when NOT to use this tool (for component selection) by explaining that components are deliberately excluded, and when to rely on this tool instead of search (for catalog-uncovered roles). This is clear usage direction.

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 and does so thoroughly. It discloses the substitution limitations, the behavior for components with zero substitutable values, the fact that structural values are never substituted and are listed under 'Valores no parametrizables', and the default behavior when `system` is omitted.

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 longer than typical but front-loaded with the deprecation warning and each sentence provides meaningful context. However, some historical details (e.g., 'judged 11/30', 'DECISIONS.md D15') are not essential for invocation and could be trimmed.

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 fully covers what the tool returns, its edge cases, and defaults. It explains both the normal substitution behavior and the no-op behavior for inapplicable components, making the tool's behavior predictable.

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 adds semantic value by explaining the `system` parameter's default ('Omit `system` to use the component's native one') and clarifying what substitution applies to. This goes beyond the bare schema 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 states clearly that the tool is deprecated in favor of get_component_prompt, and explains its intended operation: 'Returns the component's build prompt with its motion tokens (ease/duration/stagger) replaced by the chosen motion system's values.' This gives a specific verb, resource, and distinguishes it from the sibling get_component_prompt.

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?

Explicitly directs users to the alternative: 'DEPRECATED in favour of get_component_prompt.' It also explains why mechanical substitution is unsuitable for this corpus, providing clear guidance on when this tool should not be used and that components with no substitutable values will return the prompt unchanged.

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 burden. It discloses behavioral traits: returns a ranked, compact list with a 'why' per hit, and allows browsing purely by filters when query is empty. It does not mention rate limits, authentication, or read-only status, but the described behavior is adequate for a search operation.

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

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

The description is two sentences long, front-loads the main purpose, and includes practical examples and usage guidance. Every sentence earns its place: the first states purpose and output, the second explains how to use filters and the next step. No fluff 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 search tool with 9 parameters but no output schema, the description covers the essential workflow: how to formulate the search, what to expect in the result (ranked list with why), and the follow-up action (get_component). It lacks details on pagination or error handling, but the parameter schema is rich and the description is sufficient for typical usage.

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

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

The schema covers all parameters (100% coverage), so baseline is 3. The description adds value beyond the schema by explaining how to combine the query with facet filters, and provides examples of natural-language inputs. It also clarifies that query is optional ('Leave query empty to browse purely by filters'), which is not explicit in the schema alone.

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 clearly identifies the resource ('components'). It distinguishes itself from siblings by focusing on natural-language briefs or name/alias search, with examples ('un hero elegante...') and a ranked compact list with a 'why' field, which is unique among the listed 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?

It provides clear usage context: combine the free-text query with facet filters to narrow results, and then call get_component on the chosen slug. It also states the behavior when query is left empty ('browse purely by filters'). However, it does not explicitly mention when not to use this tool or directly contrast it with alternatives like list_components.

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

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

No annotations are provided, so the description carries the burden. It reveals that the tool filters on measured motion metadata rather than prose, and it clarifies the semantic difference between category (appearance) and trigger/cluster (movement). However, it does not disclose how multiple filters combine (AND/OR), whether results are ranked, or what output format to expect. This is a moderate level of transparency for a search 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?

The description is two sentences, immediately front-loaded with the core behavior. The first sentence is a crisp definition; the second adds crucial usage context without redundancy. Every part earns its place, and it is not bloated.

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 7 parameters, no output schema, no annotations, and low schema coverage, the description should provide more context about filter combination logic, output shape, and what 'CAPA-1 motion block' means. The description gives good high-level guidance but leaves practical operational details unaddressed. It is not fully sufficient for an agent to invoke the tool confidently without further assumptions.

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 only 14% (libs), so the description must compensate. It names several key parameters (trigger, libs, complexity, motion_system, category) and clarifies the meaning of trigger versus category. However, it introduces 'cluster' as a concept not present in the schema, potentially confusing agents. It does not detail limit, max_complexity, or motion_system semantics, though field names are somewhat self-explanatory.

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 ('Filters') and resource ('components') with a clear scope: 'by the CAPA-1 motion block (trigger, libs, complexity, motion system) rather than by prose.' It also distinguishes itself from the sibling tool search_components by explaining the look-vs-move distinction. The title reinforces the purpose.

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?

Explicitly provides guidance: 'Prefer this over search_components when composing a page' and gives a concrete example of why the two tools disagree ('slider label alone splits across 3 opposite physics'). This effectively tells the agent when to use this tool instead of the alternative.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It transparently explains the internal logic: 'filtered first by the catalog facets that actually correspond to the role, then ranked by the free-text need.' It also discloses a key behavioral trait: the ability to return 'NOTHING' for a role rather than returning a false positive. This goes beyond a simple 'returns suggestions' and is genuinely informative, though it doesn't cover every edge case or output detail.

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 slightly verbose but each sentence adds value. It front-loads the core function, then provides a crucial differentiator ('Crucially, it can answer that the catalog has NOTHING'), and ends with actionable guidance for the 'none' verdict. The examples (counters, pricing tables, etc.) are helpful but could be trimmed without losing meaning.

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 complexity (6 parameters, no output schema) and its role in the planning workflow, the description covers the essential context: purpose, usage context, and alternative behavior. It mentions the 'adopted' parameter indirectly through the contrast with get_integration_contract, and it explains what to do on 'none.' It doesn't detail the return schema, but that is fine since no output schema exists. Overall, it's a well-rounded 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?

The input schema already provides detailed descriptions for all 6 parameters, covering 100% of them. The description adds a little by connecting 'section role' and 'what that section has to do' to the role and need parameters, but it doesn't elaborate on the other parameters, and the schema already does that heavy lifting. A score of 3 is the baseline for high schema coverage, and the description adds marginal value above it.

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

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

The description clearly states the tool's function: 'Given a section role from plan_page and what that section has to do, returns ranked candidate components.' It uses a specific verb ('returns'), names the resource ('candidate components'), and explains the filtering and ranking process. It also distinguishes itself from search_components by highlighting its ability to return a 'none' verdict, which is a unique capability.

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

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

The description gives explicit usage guidance: 'Prefer this over search_components when building a planned page.' It also explains when search_components is insufficient and provides an alternative action for when the verdict is 'none': 'write the section from the motion system tokens.' This clearly outlines when to use the tool and what to do in a specific outcome.

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 full responsibility for behavioral disclosure. It reveals that some kinds return a 'not-a-component' verdict instead of a candidate list, that treatments add on top without consuming sections, and that they compete for capabilities like scroll owner and document height. This goes beyond basic description to expose important edge-case 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 somewhat long but every sentence provides valuable context: purpose, historical rationale, behavioral exceptions, and integration guidance. It is front-loaded with the key distinction from suggest_mechanics. Slightly verbose due to the historical aside, but still efficient.

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

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

For a tool with no output schema and no annotations, the description covers purpose, scope, behavior, and integration requirements comprehensively. It explains the conceptual model (page vs section), constraints (not consuming sections, not counting toward motion_coverage), and special output types. An agent has enough information to select and invoke this 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?

The input schema provides 100% coverage with descriptions for all parameters. The description adds some high-level context (e.g., scroll-suave is a decision rather than a component), but it does not need to explain parameters since the schema already does. Baseline 3 is appropriate.

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

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

The description explicitly states it is 'The twin of suggest_mechanics for the family that does not fill a section slot' and lists concrete page-level treatments (smooth scroll, shaped edge, page background, custom cursor, route transitions). This clearly identifies the tool's function and differentiates it from the sibling suggest_mechanics.

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 names the sibling tool as an alternative and clarifies the scope (page-level vs section-level). It also provides specific guidance: 'A treatment does NOT consume a section and does NOT count toward motion_coverage' and advises passing adopted treatments to get_integration_contract, giving the agent concrete when-to-use context.

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