Get exact props (with types and defaults), code examples, composition patterns, and common mistakes for a single @marmoui/ui component. REQUIRED before using any component for the first time in a session — prevents hallucinated props (e.g. variant="danger" on Badge, size="lg" on Text) that fail at build time.
| Name | Required | Description | Default |
|---|---|---|---|
| componentName | Yes | Name of the component to get information about (e.g. "Button", "Card", "Input") |
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?
There are no annotations, so the description carries the full burden. It discloses the tool's output (props, types, defaults, examples, composition, mistakes) and its mandatory role in the workflow. It also warns about build-time failures from skipping it, adding useful behavioral context. It doesn't mention auth or side effects, but as a read-only info tool this is adequate.
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, both purposeful. The first sentence front-loads the action and deliverables. The second sentence adds a clear usage mandate and rationale. No redundancy or filler.
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?
With a single simple parameter, no output schema, and no annotations, the description needs to explain what the tool returns and when to use it. It lists the content types (props, types, defaults, examples, composition, mistakes) and the timing requirement. This fully covers the necessary context for an agent to use the tool 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 input schema already provides a clear description for componentName with examples (e.g. 'Button', 'Card', 'Input'). Since schema description coverage is 100%, the tool description adds no additional parameter-level meaning. Baseline 3 is appropriate.
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 function with a specific verb and resource: 'Get exact props (with types and defaults), code examples, composition patterns, and common mistakes for a single @marmoui/ui component.' It distinguishes itself from siblings like list_components and search_components by focusing on detailed info for a single named component.
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 when-to-use guidance: 'REQUIRED before using any component for the first time in a session.' It also explains the benefit (prevents hallucinated props that fail at build time). However, it does not explicitly mention when not to use it or name alternatives, so it lacks exclusionary 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.
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.