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search_components

Fuzzy-search the component catalog by free-text keyword to find matches by name, category, description, or design inspiration. Use this to discover components when you know the use case but not the component name.

Instructions

Fuzzy-search the UploadKit component catalog by any free-text keyword — component name, category, description, or design inspiration (e.g. "apple", "stripe", "vercel", "terminal", "progress ring", "kanban board", "matrix").

When to use: the user describes the vibe or use case but does not know the component name yet ("I want something like Stripe Checkout", "show me Apple-style uploaders"). Prefer this over list_components when the goal is discovery rather than enumeration.

Returns: JSON { query, count, matches: [{ name, category, description, inspiration }] }. Read-only, idempotent, case-insensitive.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesFree-text search string. Case-insensitive substring match against name, category, description, and inspiration fields. Examples: "terminal", "apple", "progress ring", "kanban", "vercel", "matrix".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.1
    • changedInput schema / properties / query / description
      Previous value: -"Free-text search."New value: +"Free-text search string. Case-insensitive substring match against name, category, description, and inspiration fields. Examples: \"terminal\", \"apple\", \"progress ring\", \"kanban\", \"vercel\", \"matrix\"."
  2. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

No annotations are provided, so the description takes on the responsibility. It discloses 'Read-only, idempotent, case-insensitive' and specifies the exact JSON return format, providing solid transparency. Minor omissions like pagination or result limits are acceptable 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.

Conciseness4/5

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

The description is moderately long but well-structured with clear sections (core function, when to use, return format). It front-loads the purpose and uses whitespace effectively. A few redundant phrases could be trimmed, but overall efficient.

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

Completeness5/5

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

Considering the lack of annotations and output schema, the description provides comprehensive context: purpose, usage guidance, return structure, and safety profile. It fully equips an agent to decide when and how to invoke the tool, including distinguishing from siblings.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds value by introducing the fuzzy-search concept, the 'design inspiration' interpretation, and examples like 'apple' and 'vercel' that go beyond the schema examples. This elevates it above baseline.

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 clearly states 'Fuzzy-search the UploadKit component catalog by any free-text keyword' and enumerates the fields searched (name, category, description, inspiration). It distinguishes from sibling list_components by explicitly positioning it as discovery-focused.

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

Usage Guidelines5/5

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

Contains an explicit 'When to use' section with concrete example user queries and an explicit instruction to prefer this over list_components when the goal is discovery rather than enumeration. This directly addresses tool selection.

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