Get a ready-made AI-generated icon pack (12 consistent icons) — e.g. a house style like 'clay-starter' / 'line-minimal-starter', or a pack generated in the MEASURED style of a decoded brand. Free and unmetered. Each image is a 1024px transparent PNG you can download and use directly (full commercial rights). Unknown slug → lists available packs.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does an excellent job. It discloses that the tool is free and unmetered, returns 1024px transparent PNGs, grants full commercial rights, and lists available packs for unknown slugs. This is rich behavioral context beyond the basic getter nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with purpose, followed by examples and key details about format, licensing, and error behavior. Every sentence earns its place with no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema, the description covers purpose, usage context, exact output format (PNG, 1024px), licensing, and fallback behavior. It provides sufficient information for an agent to invoke and interpret the result 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 schema has only a slug parameter with 0% coverage, and the description fully compensates by giving concrete examples ('clay-starter', 'line-minimal-starter') and explaining that unknown slugs trigger a list of available packs. This adds meaning far beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: retrieving a ready-made AI-generated icon pack. It specifies the resource (icon pack) and differentiates it from sibling get_* tools by emphasizing the 12 consistent icons, PNG format, and the option to list available packs for unknown slugs.
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 for when to use this tool (when needing a ready-made icon pack, with examples of house styles and brand-specific packs) and highlights free/unmetered access. However, it does not explicitly mention alternatives or exclusion cases, such as when to use generate_asset instead, which would qualify for a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The compare_* / get_* / search_* family creates real overlap: compare_components, get_component, get_recipe, and search_screens(kind="component") can all answer similar component questions, and the drift family (get_design_drift, get_design_history, list_design_changes) requires careful reading to pick the right one. However, the detailed descriptions mostly draw clear lines between cross-product comparison, single-spec retrieval, and corpus-level search.
The server mostly follows a clean verb_noun convention: get_*, compare_*, list_*, search_*, validate_design, generate_asset. The pattern is highly consistent, though a few names differ slightly in style (audit_code vs validate_design vs get_score), and pluralization varies in tools like compare_components and compare_sections.
At 23 tools this is on the heavy side, and several calls overlap in scope enough to feel redundant. That said, the server's broad purpose suggests a design system reference plus audit platform, so the count is justifiable; it could be consolidated into a tighter 15-18 set.
The surface covers design system retrieval, component/section/recipe specs, screens and flows, search, audit/tools, icon assets, and drift/history of measured design tokens, leaving few cap gaps for the declared domain. Minor gaps remain around some metadata like direct screenshot banding by product, but no major dead-end workflow is apparent.
