List YOUR private design systems (BYODS) — the sites you decoded into your own/your team's scope. Each item has name + slug; pass the slug/name to get_design_system or get_product to build against it. Returns an empty list if you have none (or aren't authenticated).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses that the tool returns an empty list if the user has no private design systems or is not authenticated, which is a key behavioral trait. It also reveals that each item only contains name and slug, setting expectations for the response structure. However, it does not elaborate on authentication requirements beyond the empty list case, nor any other edge behaviors like rate limits or ordering, but for a simple list operation this is reasonably transparent.
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-loaded with the core purpose, and every clause adds meaningful information: the scope ('YOUR private'), the item structure ('name + slug'), the integration with other tools, and the edge case for an empty list. No redundant phrases exist, making it highly concise and well-structured.
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?
Despite having no output schema and only minimal formal metadata, the description fully explains the return value (each item has name + slug; empty list when none/not authenticated). It provides enough context for an agent to invoke the tool and understand the result. The tool's simplicity means the description covers all essential aspects without needing extra detail, so it is complete for its purpose.
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 parameter semantics are not applicable in the traditional sense. The description adds value by explaining the output format (name + slug) and how those values are used, which compensates for the lack of an output schema. Since there are no params, the baseline is 4, and the description meets this by not omitting any parameter-related meaning.
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 it lists the user's private design systems, using the verb 'List' and specifying the resource ('YOUR private design systems'). The phrase 'BYODS' and the distinction from other tools (via 'private' and 'your') help differentiate it from sibling tools like get_design_system. It also explains what each item contains and how to use it, leaving no ambiguity about the tool's function.
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 guidance on when to use this tool: to list private design systems, and it directly tells the user to pass the slug/name to get_design_system or get_product for further actions. It also warns about the empty list when not authenticated, which is a clear context signal. However, it doesn't explicitly mention when to NOT use this tool or compare it to alternative list methods, so it lacks a bit of exclusion guidance.
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.
