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multi_platform_reviews

Read-only

Retrieve combined ratings and review counts from Google and Trustpilot for any business, with per-platform details.

Instructions

Get review data across multiple platforms (Google, Trustpilot). Returns per-platform ratings and counts with a combined score. Costs 6 credits. Note: this tool queries multiple review sources and may take 10-30 seconds to return — this is normal, not an error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
business_nameYesBusiness name
locationYesCity and state
platformsNoPlatforms to check. Default: ["google", "trustpilot"]

Implementation Reference

  • The handler function that executes the multi_platform_reviews tool logic. Calls the API at /v1/reviews/multi-platform with business_name, location, and optional platforms array, then formats the result.
    withErrorHandling(async ({ business_name, location, platforms }) => {
      const result = await callApi(
        "/v1/reviews/multi-platform",
        { business_name, location, ...(platforms && { platforms }) },
        getAuth(),
        120_000
      );
      return { content: [{ type: "text" as const, text: formatResult(result.data, result) }] };
    })
  • Zod schema defining the input parameters for the multi_platform_reviews tool: business_name (required string), location (required string), and platforms (optional array of 'google' | 'trustpilot').
    {
      business_name: z.string().describe("Business name"),
      location: z.string().describe("City and state"),
      platforms: z.array(z.enum(["google", "trustpilot"])).optional().describe('Platforms to check. Default: ["google", "trustpilot"]'),
    },
  • Registration of the 'multi_platform_reviews' tool via server.tool() within the registerReviewTools function. This is how the tool is registered with the MCP server.
    server.tool(
      "multi_platform_reviews",
      "Get review data across multiple platforms (Google, Trustpilot). Returns per-platform ratings and counts with a combined score. Costs 6 credits. Note: this tool queries multiple review sources and may take 10-30 seconds to return — this is normal, not an error.",
      {
        business_name: z.string().describe("Business name"),
        location: z.string().describe("City and state"),
        platforms: z.array(z.enum(["google", "trustpilot"])).optional().describe('Platforms to check. Default: ["google", "trustpilot"]'),
      },
      READ_ONLY,
      withErrorHandling(async ({ business_name, location, platforms }) => {
        const result = await callApi(
          "/v1/reviews/multi-platform",
          { business_name, location, ...(platforms && { platforms }) },
          getAuth(),
          120_000
        );
        return { content: [{ type: "text" as const, text: formatResult(result.data, result) }] };
      })
    );
  • src/server.ts:37-37 (registration)
    Top-level registration call in createMcpServer that triggers registration of all review tools (including multi_platform_reviews).
    registerReviewTools(server, getAuth);
  • Helper wrapper (withErrorHandling) that catches errors thrown by the handler and returns them as MCP error content instead of crashing.
    export function withErrorHandling<T>(
      fn: (args: T) => Promise<ToolResult>
    ): (args: T) => Promise<ToolResult> {
      return async (args) => {
        try {
          return await fn(args);
        } catch (err) {
          const message = err instanceof Error ? err.message : String(err);
          console.error(`[mcp] Tool error: ${message}`);
          return {
            content: [{ type: "text" as const, text: `Error: ${message}` }],
            isError: true,
          };
        }
      };
    }
Behavior4/5

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

Annotations indicate read-only, non-destructive, open-world. The description adds value beyond annotations by disclosing the credit cost and significant latency, which are critical behavioral traits for an AI agent. No contradiction with annotations.

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?

Two sentences, no fluff. The core purpose is front-loaded, followed by essential usage notes (cost, latency). Every part of the description serves a purpose.

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 no output schema, the description adequately hints at return structure. It covers purpose, parameters (implicitly via schema), cost, latency, and behavior. Could perhaps mention that results are per-platform, but overall complete for a review aggregation tool with moderate complexity.

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?

All three parameters have schema descriptions (100% coverage), so the description does not need to add parameter-level detail. It provides output context ('per-platform ratings and counts with a combined score') but no additional per-parameter semantics. 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's verb ('Get'), resource ('review data across multiple platforms'), and specific platforms ('Google, Trustpilot'). It distinguishes itself from siblings like 'google_reviews' by explicitly mentioning multiple platforms and a combined score.

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?

Provides clear guidance on cost (6 credits) and expected latency (10-30 seconds), including a note that the delay is normal. While it doesn't explicitly contrast with alternatives, the sibling context (e.g., google_reviews) implicitly suggests when to use this vs. single-platform tools.

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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