Skip to main content
Glama
TheValkDokk

appstore-data

Search apps

search_apps
Read-only

Search Apple App Store or Google Play by keyword and get compact result rows with app IDs for follow-up detail requests.

Instructions

Search a store by keyword, using the store's own relevance ranking. Returns compact rows with the id you need for get_app.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoLanguage code, e.g. en, es, fr, ja. Defaults to en.en
limitNoNumber of results, 1-50. Defaults to 10.
queryYesKeyword(s) to search for, e.g. 'meditation', 'photo editor'.
storeNoStore to query: "ios" (Apple App Store) or "android" (Google Play).ios
countryNoISO-2 storefront code, e.g. us, gb, de, jp. Defaults to us.us

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover read-only and open-world safety. The description adds real value beyond them by disclosing that results use the store's own relevance ranking and come back as compact rows, which tells the agent not to expect full app records or a deterministic ordering.

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 tight sentences, zero filler, with the core action front-loaded and the follow-up pointer second. Every clause earns its place.

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?

With no output schema, the description compensates by characterizing the return shape ('compact rows with the id') and its intended downstream use. An agent has enough to call it correctly; only the browse-vs-search distinction from siblings is missing.

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%, with lang, limit, store, country, and query all documented including defaults and enum values. The description only reinforces the keyword concept behind query and adds nothing on syntax or store/country interplay, so baseline 3 applies.

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?

States a specific verb and resource ('Search a store by keyword') plus the ranking behavior, and explicitly ties results to the get_app follow-up. It doesn't differentiate from siblings like top_charts or list_categories (browse vs. search), so it falls just short of a 5.

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 second sentence implies the workflow ('the id you need for get_app'), which is useful chaining guidance. However, there is no explicit statement of when to use this versus top_charts or list_categories, and no exclusion conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.