Generate a portable DESIGN.md for the authenticated tenant — brand color, type, tokens, and component inventory — that any AI agent can read to generate on-brand UI. Pass format="markdown" (default) for the DESIGN.md string or format="json" for structured data. Output is deterministic for a given tenant.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Output format. "markdown" returns the DESIGN.md string; "json" returns structured data. | markdown |
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, the description carries the full burden. It discloses that the output is deterministic for a given tenant, portable, and readable by any AI agent. It also outlines what content is included. It does not explicitly state that the operation is read-only, but the generation semantics and lack of mutation hints make it 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 three sentences, front-loaded with the core purpose, then format guidance, then a behavioral note about determinism. Every sentence adds value and there is no fluff or repetition.
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, the description explains both return types (markdown string or structured data) and highlights key behavioral aspects (determinism, portability) that are essential for an agent. It is complete for a simple single-parameter tool, though it could mention error scenarios or authentication requirements beyond 'authenticated tenant'.
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 schema has 100% coverage with a detailed description for the 'format' parameter. The tool description essentially repeats the schema description ('Pass format="markdown" (default) for the DESIGN.md string or format="json" for structured data'), adding no extra meaning. A baseline of 3 is appropriate when the schema already documents the parameters.
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 a specific verb ('Generate') and resource ('portable DESIGN.md') with detailed content (brand color, type, tokens, component inventory). It distinguishes itself from sibling tools like get_design_guidelines and get_tenant_theme by focusing on a consolidated, agent-readable design document.
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 context: it is meant for generating a portable DESIGN.md for an AI agent. It explains format options and defaults, implying when to use markdown vs json. However, it does not explicitly mention exclusions or alternatives, such as when to use get_design_guidelines or get_tenant_theme instead.
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.