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VanguardiaAI

motionprompts MCP

by VanguardiaAI

Search components

search_components

Find the right GSAP component from a natural-language brief or name, then filter by mood, use case, level, and more to get ranked results with reasons.

Instructions

Find the components that best fit a natural-language brief (e.g. "un hero elegante para una marca de lujo", "cartas apiladas al hacer scroll") or a name/alias. Returns a ranked, compact list with a why for each hit. Combine the free-text query with facet filters to narrow. Then call get_component on the chosen slug. Leave query empty to browse purely by filters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depsNoRequire these runtime deps (e.g. ["three"], ["lenis"]). AND semantics.
moodNoAesthetic filter. One of: minimal, elegant, luxury, editorial, bold, playful, corporate, retro, futuristic, experimental, cinematic, brutalist, organic, technical
levelNopage (owns the whole page/scroll) | section (a block) | widget (small embeddable).
limitNoMax results (default 12).
queryNoFree-text brief or component name/alias. Spanish or English; accent-insensitive.
useCaseNoPage-role filter. One of: hero, preloader, page-transition, navigation, footer, gallery, slider, card-deck, scroll-story, image-reveal, text-reveal, hover-interaction, cursor-effect, logo, background, product-showcase, marquee, team-section, cta, contact
categoryNoAnimation-family filter (e.g. scroll, cards, menu, 3d-webgl).
mobileSafeNoIf true, only components that degrade well on touch/small screens.
maxPerfCostNoCap runtime weight: light | medium | heavy.
Behavior4/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters4/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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