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JiantaoFu

App Market Intelligence MCP

google-play-suggest

Retrieve Google Play search suggestions to identify trending keywords and improve app discoverability for market research.

Instructions

Get search suggestions from Google Play. Returns an array of suggested search terms (up to 5). Sample response: ['panda pop', 'panda', 'panda games', 'panda run', 'panda pop for free']

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
termYesSearch term to get suggestions for (e.g., 'panda')
langNoLanguage code for suggestions (default: en)en
countryNoCountry code to get suggestions from (default: us)us

Implementation Reference

  • Handler function that fetches search suggestions using gplay.suggest and returns them as a JSON string in the MCP response format.
    async ({ term, lang, country }) => {
      const suggestions = await gplay.suggest({ term, lang, country });
      // API returns array of strings directly
      return { content: [{ type: "text", text: JSON.stringify(suggestions) }] };
    }
  • Zod input schema defining parameters: term (string, required), lang (string, default 'en'), country (string, default 'us').
    {
      term: z.string().describe("Search term to get suggestions for (e.g., 'panda')"),
      lang: z.string().default("en").describe("Language code for suggestions (default: en)"),
      country: z.string().default("us").describe("Country code to get suggestions from (default: us)")
    }, 
  • src/server.js:587-600 (registration)
    Full server.tool registration call that defines the tool name, description, input schema, and inline handler function.
    server.tool("google-play-suggest", 
      "Get search suggestions from Google Play. Returns an array of suggested search terms (up to 5).\n" +
      "Sample response: ['panda pop', 'panda', 'panda games', 'panda run', 'panda pop for free']",
      {
        term: z.string().describe("Search term to get suggestions for (e.g., 'panda')"),
        lang: z.string().default("en").describe("Language code for suggestions (default: en)"),
        country: z.string().default("us").describe("Country code to get suggestions from (default: us)")
      }, 
      async ({ term, lang, country }) => {
        const suggestions = await gplay.suggest({ term, lang, country });
        // API returns array of strings directly
        return { content: [{ type: "text", text: JSON.stringify(suggestions) }] };
      }
    );
Behavior3/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It adds useful context about the return format ('array of suggested search terms'), quantity limit ('up to 5'), and provides a concrete sample response. However, it doesn't mention rate limits, authentication requirements, error conditions, or whether this is a read-only operation (though 'Get' implies 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 perfectly concise with two sentences that each earn their place. The first sentence states the purpose and return format, while the second provides a concrete example that helps users understand the output. No wasted words or redundant 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?

For a simple read-only lookup tool with 3 well-documented parameters and no output schema, the description provides adequate context. It explains what the tool does, shows the return format with a sample, and mentions the result limit. The main gap is lack of explicit behavioral constraints (rate limits, auth needs) which would be helpful but isn't critical for this type of tool.

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%, so the schema already fully documents all three parameters. The description adds no additional parameter information beyond what's in the schema. According to guidelines, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Get search suggestions from Google Play' with the verb 'Get' and resource 'search suggestions'. It distinguishes from siblings like 'google-play-search' by focusing on autocomplete suggestions rather than full search results. However, it doesn't explicitly contrast with 'app-store-suggest' which serves a similar function for a different platform.

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 context through the sample response showing autocomplete suggestions, suggesting this tool is for getting search term predictions. However, it provides no explicit guidance on when to use this versus alternatives like 'google-play-search' or 'app-store-suggest', nor does it mention any prerequisites or 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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