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maasy_get_brand_context

Retrieve complete brand DNA including name, industry, tone, ideal customer profile, value proposition, assets, and references to ensure consistent, on-brand content generation.

Instructions

Full brand DNA: name, industry, tone, ICP, value prop, assets, references. Essential for on-brand generation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idNoBrand UUID

Implementation Reference

  • src/index.ts:76-81 (registration)
    Registration of the 'maasy_get_brand_context' tool on the MCP server with its description ('Full brand DNA...') and schema (optional project_id). The handler is delegated to toolHandler('get_brand_context').
    server.tool(
      "maasy_get_brand_context",
      "Full brand DNA: name, industry, tone, ICP, value prop, assets, references. Essential for on-brand generation.",
      { project_id: z.string().optional().describe("Brand UUID") },
      toolHandler("get_brand_context")
    );
  • Input schema for the tool: optional project_id (string) with description 'Brand UUID'.
    { project_id: z.string().optional().describe("Brand UUID") },
  • Generic toolHandler wrapper that delegates to callGateway(toolName, args). For 'maasy_get_brand_context', it calls callGateway('get_brand_context', args) which makes a POST to the Supabase mcp-gateway edge function.
    function toolHandler(toolName: string, argsFn?: (args: Record<string, unknown>) => Record<string, unknown>) {
      return async (args: Record<string, unknown>) => {
        try {
          const gatewayArgs = argsFn ? argsFn(args) : args;
          // Auto-inject default project_id if not provided
          if (DEFAULT_PROJECT_ID && !gatewayArgs.project_id) {
            gatewayArgs.project_id = DEFAULT_PROJECT_ID;
          }
          const result = await callGateway(toolName, gatewayArgs);
          return { content: [{ type: "text" as const, text: JSON.stringify(result, null, 2) }] };
        } catch (e: unknown) {
          return {
            content: [{ type: "text" as const, text: `Error: ${e instanceof Error ? e.message : String(e)}` }],
            isError: true,
          };
        }
      };
    }
  • The callGateway function sends a POST request to the configured gateway URL with the tool name and arguments. This is the actual network call that executes the tool logic server-side (the real 'get_brand_context' logic lives in the Supabase edge function, not in this codebase).
    export async function callGateway(tool: string, args: Record<string, unknown> = {}): Promise<unknown> {
      const res = await fetch(gatewayUrl, {
        method: "POST",
        headers: {
          "Content-Type": "application/json",
          [authHeader.name]: authHeader.value,
        },
        body: JSON.stringify({ tool, args }),
      });
    
      const data = await res.json();
    
      if (!res.ok) {
        throw new Error(data.error || `Gateway error (${res.status})`);
      }
    
      return data.result;
    }
  • The 'active-brand' resource also calls callGateway('get_brand_context') with the default project ID, reusing the same backend logic.
      try {
        const ctx = await callGateway("get_brand_context", { project_id: DEFAULT_PROJECT_ID });
        return { contents: [{ uri: uri.href, mimeType: "application/json", text: JSON.stringify(ctx, null, 2) }] };
      } catch (e: unknown) {
        return {
          contents: [
            { uri: uri.href, mimeType: "text/plain", text: `Error: ${e instanceof Error ? e.message : String(e)}` },
          ],
        };
      }
    });
Behavior3/5

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

No annotations are provided, so the description must carry all behavioral information. It states what data is returned but does not disclose read-only nature, performance characteristics, or effects when project_id is omitted.

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?

One clear sentence front-loads the purpose, followed by essential context. No unnecessary information.

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

Completeness4/5

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

Given the tool has one parameter and no output schema, the description sufficiently explains the return value and use case. However, it does not clarify behavior when project_id is missing, nor does it mention prerequisites or relation to sibling tools.

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?

Input schema covers 100% of parameters with a description ('Brand UUID'), so baseline is 3. The description does not add any additional semantic meaning beyond the schema for the parameter.

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?

Description clearly defines the tool's function: retrieving full brand details (name, industry, etc.). It uses specific verbs and nouns, and differs from siblings like maasy_list_brands which lists brands without context.

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

Usage Guidelines4/5

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

Implicitly indicates use for 'on-brand generation', but does not explicitly state when to use versus alternatives like maasy_list_brands or maasy_get_skill. Given the hint, it is clear but lacks detailed exclusions.

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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