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review_velocity

Read-only

Track review velocity trends over time, including monthly counts, rating changes, reply rates, and sentiment themes. Identify patterns in your Google Business Profile reviews.

Instructions

Analyze review velocity trends over time. Returns reviews per month, rating trend, reply rate, sentiment themes, and monthly breakdown. Costs 6 credits. Note: this tool may take 10-30 seconds to return — this is normal, not an error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
business_nameYesBusiness name
locationYesCity and state
periodNoTime period: "30d", "90d", "6m", or "1y". Default: "90d"

Implementation Reference

  • The tool 'review_velocity' is registered on the MCP server using server.tool() with the name 'review_velocity', description, input schema, and handler.
    server.tool(
      "review_velocity",
      "Analyze review velocity trends over time. Returns reviews per month, rating trend, reply rate, sentiment themes, and monthly breakdown. Costs 6 credits. Note: this tool 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"),
        period: z.string().optional().describe('Time period: "30d", "90d", "6m", or "1y". Default: "90d"'),
      },
      READ_ONLY,
      withErrorHandling(async ({ business_name, location, period }) => {
        const result = await callApi(
          "/v1/reviews/velocity",
          { business_name, location, ...(period && { period }) },
          getAuth(),
          120_000
        );
        return { content: [{ type: "text" as const, text: formatResult(result.data, result) }] };
      })
    );
  • The handler function for 'review_velocity' calls the API endpoint '/v1/reviews/velocity' with business_name, location, and optional period parameters, wrapped in withErrorHandling for error management. Uses a 120-second timeout.
      withErrorHandling(async ({ business_name, location, period }) => {
        const result = await callApi(
          "/v1/reviews/velocity",
          { business_name, location, ...(period && { period }) },
          getAuth(),
          120_000
        );
        return { content: [{ type: "text" as const, text: formatResult(result.data, result) }] };
      })
    );
  • The input schema for 'review_velocity' defines required parameters business_name (string) and location (string), and optional period (string) with values '30d', '90d', '6m', or '1y'.
    {
      business_name: z.string().describe("Business name"),
      location: z.string().describe("City and state"),
      period: z.string().optional().describe('Time period: "30d", "90d", "6m", or "1y". Default: "90d"'),
    },
  • The registerReviewTools function is exported and called from src/server.ts, which registers the review_velocity tool along with other review tools.
    export function registerReviewTools(server: McpServer, getAuth: () => string) {
      server.tool(
        "google_reviews",
        "Get Google reviews for a business. Returns review text, rating, date, author, and owner replies. Costs 1 credit per 10 reviews. Note: this tool 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"),
          limit: z.number().int().min(1).max(100).optional().describe("Number of reviews (1-100). Default: 10"),
          sort: z.enum(["newest", "highest", "lowest", "most_relevant"]).optional().describe("Sort order. Default: newest"),
        },
        READ_ONLY,
        withErrorHandling(async ({ business_name, location, limit, sort }) => {
          const result = await callApi(
            "/v1/reviews/google",
            { business_name, location, ...(limit && { limit }), ...(sort && { sort }) },
            getAuth()
          );
          return { content: [{ type: "text" as const, text: formatResult(result.data, result) }] };
        })
      );
    
      server.tool(
        "review_velocity",
        "Analyze review velocity trends over time. Returns reviews per month, rating trend, reply rate, sentiment themes, and monthly breakdown. Costs 6 credits. Note: this tool 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"),
          period: z.string().optional().describe('Time period: "30d", "90d", "6m", or "1y". Default: "90d"'),
        },
        READ_ONLY,
        withErrorHandling(async ({ business_name, location, period }) => {
          const result = await callApi(
            "/v1/reviews/velocity",
            { business_name, location, ...(period && { period }) },
            getAuth(),
            120_000
          );
          return { content: [{ type: "text" as const, text: formatResult(result.data, result) }] };
        })
      );
  • The withErrorHandling helper wraps the tool handler to catch errors and return them as MCP error content.
    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?

Beyond annotations (readOnlyHint=true, destructiveHint=false), the description adds credit cost (6 credits) and latency warning (10-30 seconds). This is valuable behavioral context. 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?

The description is highly concise with two sentences and a note. The first sentence front-loads the purpose and expected return data; the second covers cost and latency. No extraneous text.

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?

No output schema exists, but the description lists several return fields (reviews per month, rating trend, etc.), providing a good mental model. It balances completeness without being overly verbose.

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 has 100% description coverage, so the schema itself documents parameters adequately. The description does not add extra meaning beyond the schema, meriting the baseline score of 3.

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 analyzes review velocity trends over time, specifying the action ('Analyze') and resource ('review velocity'). It lists specific outputs, making its purpose distinct from sibling tools like google_reviews or reputation_audit.

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

Usage Guidelines2/5

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

The description lacks explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or comparative contexts. The purpose is implied but not formally differentiated.

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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