Returns the MCP knowledge version: gitSha, indexedAt, componentCount, patternCount, uptimeSeconds. Call this ONCE per session before generating UI code so you know how fresh the design-system data is. Cheap to call. If gitSha is "unknown" or indexedAt is far in the past, surface that to the user before relying on the data.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 behavioral burden. It discloses that the call is 'Cheap to call' (performance cost), and explains how to interpret the results, including a conditional action ('surface that to the user'). While it doesn't explicitly state read-only safety, the nature of returning metadata and the advice to surface issues implies non-destructive behavior. It adds useful context beyond a bare return-type statement.
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: the first states the purpose and output, the second provides timing guidance, and the third covers conditional handling. Every sentence adds distinct value with no wasted words. It is front-loaded with the primary action and output, making it instantly scannable.
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, zero-parameter tool with no output schema, the description is fully adequate. It covers what is returned, when to call it, the cost implication, and how to handle stale data. There are no missing prerequisites, side effects, or response-format concerns that need addressing for this tool to be used 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 has zero parameters, and the context signal shows 100% schema description coverage. The baseline for 0 params is 4. The description adds no parameter details, which is appropriate because there are none to describe. It also doesn't need to compensate for any missing parameter information.
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 begins with 'Returns the MCP knowledge version' and enumerates the specific fields (gitSha, indexedAt, componentCount, patternCount, uptimeSeconds), making the resource and output explicit. This clearly distinguishes it from sibling tools, none of which appear to provide version or freshness metadata.
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?
Explicit usage guidance is given: 'Call this ONCE per session before generating UI code so you know how fresh the design-system data is.' It also instructs when to surface a warning to the user if the data is stale, covering when to act on the result. No alternatives are needed since no sibling tool serves a similar purpose.
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.