Search @marmoui/ui by keyword (e.g. "form", "table", "dialog", "avatar"). Preferred discovery tool — returns the 5-8 most relevant components for a need. Call this before get_component_info when unsure which component to use.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum results to return (default: 10) | |
| query | Yes | Search query — matches against component names, descriptions, categories, and prop names |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the behavior of returning the '5-8 most relevant components', but this conflicts with the schema's default limit of 10, creating ambiguity. With no annotations, the description must fully describe behavior, and this inconsistency is a gap.
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 the action and purpose, and includes a clear usage directive. No filler 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, it explains when to use it, what it returns, and how it relates to get_component_info. However, it does not clarify the discrepancy between the stated '5-8' results and the limit parameter, nor does it distinguish from list_components, leaving slight gaps.
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 covers both parameters with descriptions, and the description adds example keywords. However, it does not elaborate on the limit parameter or the matching behavior beyond schema, so it adds minimal value beyond the baseline.
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 searches @marmoui/ui by keyword for relevant components, using specific examples like 'form' and 'table'. It distinguishes itself from get_component_info by advising to call it first when unsure which component to use.
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 it is the 'Preferred discovery tool' and instructs to call it before get_component_info when in doubt. This provides clear when-to-use guidance relative to a sibling tool.
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.
Most tools have distinct purposes, but review_generated_code and validate_component_usage are very similar in functionality, differentiated only by intended usage context. list_components and search_components also have some overlap, though descriptions clarify their preferred use cases.
All tool names follow a consistent verb_noun snake_case pattern (e.g., get_component_info, list_components, review_generated_code). The verbs are clear and the naming convention is uniform throughout.
14 tools is well within the ideal 3-15 range and each tool serves a distinct role in the workflow: discovery, guidance, generation, validation, and prototype sharing. The count feels appropriate for the server's comprehensive purpose.
The server covers the full generation workflow: discover components, get guidelines, generate code, validate, and deploy/share. However, there is no tool to retrieve or list saved custom components, and prototype management is limited to deploy and feedback, leaving minor gaps in persisted resource handling.