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maasy_scan_brand

Scan brand health across DNA completeness, campaigns, content, CRM, SEO, and alerts. Receive traffic-light status for each area to quickly identify issues and optimize marketing.

Instructions

Deep health scan: DNA completeness, campaigns, content, CRM, SEO, alerts. Returns traffic-light status per area.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idNoBrand UUID (uses default if omitted)

Implementation Reference

  • src/index.ts:69-74 (registration)
    Registration of the 'maasy_scan_brand' MCP tool using server.tool(). It accepts an optional project_id and delegates to toolHandler('scan_brand').
    server.tool(
      "maasy_scan_brand",
      "Deep health scan: DNA completeness, campaigns, content, CRM, SEO, alerts. Returns traffic-light status per area.",
      { project_id: z.string().optional().describe("Brand UUID (uses default if omitted)") },
      toolHandler("scan_brand")
    );
  • Generic toolHandler wrapper that calls callGateway(toolName, gatewayArgs) on the Supabase edge function 'mcp-gateway'. This is the handler invoked when 'maasy_scan_brand' is called – it passes 'scan_brand' as the tool name.
    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,
          };
        }
      };
    }
  • Input schema for maasy_scan_brand: optional project_id string validated by Zod.
    { project_id: z.string().optional().describe("Brand UUID (uses default if omitted)") },
  • The callGateway function that sends the tool name ('scan_brand') and args to the Supabase edge function gateway, which executes the actual brand scan logic server-side.
    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;
    }
Behavior3/5

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

No annotations are provided, so the description bears full burden. It discloses the output format (traffic-light status per area) and implies read-only behavior ('scan'), but does not mention side effects, performance, or authorization requirements. The description is adequate but not rich.

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 two sentences, front-loaded with the purpose and key output details. No unnecessary words.

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?

For a scan tool without output schema, the description sufficiently explains the aggregated output (traffic-light status per area) and scope. It could mention that it combines multiple sub-scans, but the sibling names already hint at individual tools. Overall, it meets the needs for selection and invocation.

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?

The input schema fully describes the only parameter (project_id) with a clear description. The tool description does not add further parameter context (e.g., examples, format). Since schema coverage is 100%, baseline 3 is appropriate.

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 performs a deep health scan across multiple brand areas (DNA, campaigns, content, CRM, SEO, alerts) and returns traffic-light status per area. This distinguishes it from siblings that focus on individual areas (e.g., maasy_get_crm_summary, maasy_get_seo_status).

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 usage for a comprehensive brand health check, but does not explicitly state when to use this tool versus individual sibling tools (e.g., for a specific area like SEO). No when-not-to-use guidance is provided.

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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