Register a prototype for sharing and get a share URL. Reviewers can leave inline comments via the Marmo overlay.
| Name | Required | Description | Default |
|---|---|---|---|
| title | No | Human-readable prototype title | |
| deployUrl | No | Deployed URL if already live | |
| prototypeId | Yes | Unique prototype identifier (slug) |
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?
With no annotations provided, the description carries the full burden. It discloses a key behavior (register and get share URL) and a feature (inline comments via Marmo overlay), but does not mention side effects, permissions, or what happens on existing prototypes. This is a moderate level of transparency.
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, each informative and front-loaded. The first sentence states the core function, the second adds a relevant detail. No wasted words.
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 registration tool with 3 parameters and no output schema, the description covers the essential context: what it does, what it returns (share URL), and an extra feature. It could be more complete about return structure or error conditions, but it is adequate for basic selection.
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 description coverage is 100%, so the schema already documents all three parameters (title, deployUrl, prototypeId) with descriptions. The tool description adds no extra parameter semantics, but the baseline of 3 is appropriate because the schema is sufficient.
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 action ('Register a prototype for sharing') and its output ('get a share URL'). It also distinguishes itself from siblings by focusing on deployment/sharing, whereas siblings like get_prototype_feedback are for retrieving feedback.
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 implies the tool is used when sharing a prototype, but it does not explicitly state when to use it versus alternatives (e.g., when to use get_prototype_feedback instead). No exclusions or prerequisites are mentioned.
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.