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JiantaoFu

App Market Intelligence MCP

app-store-suggest

Generate App Store search suggestions to discover trending app names and keywords, helping users research market demand and identify popular search terms.

Instructions

Get search suggestions from the App Store. Returns an array of objects with:

  • term: Suggested search term Each suggestion has a priority from 0 (low traffic) to 10000 (most searched)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
termYesSearch term to get suggestions for

Implementation Reference

  • The handler function for the 'app-store-suggest' tool. It calls store.suggest({ term }) from the app-store-scraper library and formats the result as MCP content.
    async ({ term, country }) => {
      const suggestions = await store.suggest({ term });
      return { content: [{ type: "text", text: JSON.stringify(suggestions) }] };
    }
  • Zod schema defining the input parameters for the tool: a required 'term' string.
    {
      term: z.string().describe("Search term to get suggestions for")
    }, 
  • src/server.js:206-217 (registration)
    The server.tool() call that registers the 'app-store-suggest' MCP tool, including its description, input schema, and handler function.
    server.tool("app-store-suggest", 
      "Get search suggestions from the App Store. Returns an array of objects with:\n" +
      "- term: Suggested search term\n" +
      "Each suggestion has a priority from 0 (low traffic) to 10000 (most searched)",
      {
        term: z.string().describe("Search term to get suggestions for")
      }, 
      async ({ term, country }) => {
        const suggestions = await store.suggest({ term });
        return { content: [{ type: "text", text: JSON.stringify(suggestions) }] };
      }
    );
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return format (array of objects with 'term' and priority) but doesn't cover critical aspects like rate limits, authentication needs, error conditions, or whether it's a read-only operation. For a tool with no annotations, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise with two sentences that efficiently convey the core functionality and return format. The bullet-point style for return details is clear, though it could be slightly more structured. There's minimal wasted text, earning a high score for efficiency.

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

Completeness3/5

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

Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is partially complete. It explains what the tool does and the return format but misses behavioral context and usage guidelines. Without annotations or output schema, more detail would be helpful, but it meets minimum viability for a simple lookup 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?

The input schema has 100% description coverage, with the single parameter 'term' clearly documented. The description adds no additional parameter semantics beyond what's in the schema. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even without parameter 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 the App Store.' It specifies the verb ('Get') and resource ('search suggestions'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'app-store-search' or 'google-play-suggest,' which would be needed for a perfect score.

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 provides no guidance on when to use this tool versus alternatives. With siblings like 'app-store-search' and 'google-play-suggest' available, there's no indication of whether this is for autocomplete, pre-search filtering, or other contexts. The lack of usage context leaves the agent guessing about appropriate scenarios.

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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