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vitrine3d

Vitrine MCP Server

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Submit Feedback

vitrine_feedback

Submit bug reports or feature requests for Vitrine. Automatically creates a GitHub issue from your feedback.

Instructions

Submit a bug report or feature request. Creates a GitHub issue automatically.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesFeedback type
messageYesDescription of the bug or feature request
pageNoWhich page/area this relates to

Implementation Reference

  • The tool handler that calls client.submitFeedback() and returns success/error text content.
      async ({ type, message, page }) => {
        const res = await client.submitFeedback(type, message, page);
        if (!res.ok) return { content: [{ type: "text", text: `Error: ${res.data?.message ?? res.status}` }], isError: true };
        return { content: [{ type: "text", text: "Feedback submitted. A GitHub issue has been created." }] };
      },
    );
  • Input schema definition for vitrine_feedback: type (enum: bug/feature/other), message (string), page (optional string).
    {
      title: "Submit Feedback",
      description: "Submit a bug report or feature request. Creates a GitHub issue automatically.",
      inputSchema: z.object({
        type: z.enum(["bug", "feature", "other"]).describe("Feedback type"),
        message: z.string().describe("Description of the bug or feature request"),
        page: z.string().optional().describe("Which page/area this relates to"),
      }),
  • The registerFeedbackTools function that calls server.registerTool() with the name 'vitrine_feedback'.
    export function registerFeedbackTools(server: McpServer, client: VitrineClient) {
      server.registerTool(
        "vitrine_feedback",
        {
          title: "Submit Feedback",
          description: "Submit a bug report or feature request. Creates a GitHub issue automatically.",
          inputSchema: z.object({
            type: z.enum(["bug", "feature", "other"]).describe("Feedback type"),
            message: z.string().describe("Description of the bug or feature request"),
            page: z.string().optional().describe("Which page/area this relates to"),
          }),
        },
        async ({ type, message, page }) => {
          const res = await client.submitFeedback(type, message, page);
          if (!res.ok) return { content: [{ type: "text", text: `Error: ${res.data?.message ?? res.status}` }], isError: true };
          return { content: [{ type: "text", text: "Feedback submitted. A GitHub issue has been created." }] };
        },
      );
    }
  • src/index.ts:43-43 (registration)
    Where registerFeedbackTools is called to wire up the tool on the MCP server.
    registerFeedbackTools(server, client);
  • VitrineClient.submitFeedback() helper method that POSTs to /v1/feedback with type, message, page, and optional meta.
    submitFeedback(type: string, message: string, page?: string, meta?: Record<string, unknown>) {
      return this.request("POST", "/v1/feedback", { type, message, page, meta });
    }
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses that it creates a GitHub issue automatically, which is a key behavioral trait. However, it does not mention authentication requirements, idempotency, rate limits, or any side effects beyond issue creation. Since no annotations are present, the description carries the full burden but only partially fulfills it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence of 10 words. It is front-loaded with the purpose ('Submit a bug report or feature request') and then adds the key behavioral detail ('Creates a GitHub issue automatically'). There is no wasted content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 3 parameters, no output schema, and no annotations. The description explains the overall outcome (issue creation) but omits details like return values, error handling, or message size constraints. It is adequate for a simple tool but leaves gaps for an agent needing full context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% (all three parameters have descriptions in the schema). The tool description adds no additional meaning to the parameters beyond what the schema already provides, so it meets the baseline but does not exceed it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool submits a bug report or feature request and creates a GitHub issue. This is a specific verb+resource combination, and it distinguishes from sibling tools which handle account info, looks, models, etc.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for submitting feedback but does not provide explicit when-to-use or when-not-to-use guidance. No alternatives are mentioned, so the agent must infer usage from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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