Validate and optionally save a custom component for the authenticated tenant. Runs review_generated_code validation before persisting. Use for AI-generated or customized components.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Generated TSX code using @marmoui/ui primitives | |
| save | No | Save as tenant component override if validation passes | |
| description | Yes | What the component should do | |
| componentName | Yes | PascalCase component name, e.g. CustomStatCard |
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does disclose the key flow: validation first, then optional persistence ('Runs review_generated_code validation before persisting'). However, it does not explain what happens if validation fails, whether saving overwrites existing components, or what the response looks like. This is partial transparency, enough for basic understanding but lacking critical side-effect details.
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 concise and well-structured. It opens with the core purpose, adds a key behavioral detail, and ends with usage guidance. All three sentences contribute unique information without redundancy, making it easy to parse and act upon.
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?
The tool has 4 parameters, no output schema, and no annotations. The description covers what the tool does, when to use it, and the validation-before-save flow, which is sufficient for selecting and invoking the tool. However, it does not describe return values or error handling, which would be useful given no output schema exists. Overall, it is reasonably complete for a simple tool, but not fully comprehensive.
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 descriptions for all 4 parameters (100% coverage), so the baseline is 3. The tool description does not add significant semantic detail about parameters; it only mentions validation before saving, which indirectly relates to the 'code' and 'save' parameters but does not elaborate on their meanings or formats. No extra value beyond the 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 function: 'Validate and optionally save a custom component for the authenticated tenant.' It uses specific verbs (validate, save) and a specific resource (custom component), and distinguishes itself from the sibling tool 'review_generated_code' by noting that it runs that validation before persisting. This makes the purpose unambiguous and differentiates it from similar tools.
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 usage context with 'Use for AI-generated or customized components,' indicating when this tool is appropriate. It also implies an alternative by mentioning it runs review_generated_code validation before persisting, but it does not explicitly state when to use review_generated_code alone or give exclusion criteria. This is clear context without explicit alternatives, earning a 4.
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.