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LaurMost

App Store MCP Server

by LaurMost

Get App Store charts

get_app_store_charts
Read-onlyIdempotent

Fetch ranked top-chart apps from the Apple App Store for a given country, category (e.g., games, productivity), and chart type (top-free, top-paid, top-grossing). Returns structured JSON with app details.

Instructions

Fetch ranked top-chart apps for a storefront. Best-effort: sourced from an undocumented Apple RSS feed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chartNoWhich chart to fetch: 'top-free', 'top-paid', or 'top-grossing'.top-free
limitNoMax entries to return (1-100).
countryNoISO 3166-1 alpha-2 storefront code, e.g. 'us', 'de', 'jp'.us
categoryNoOptional filter - a numeric App Store genre ID, or one of: books, business, developer-tools, education, entertainment, finance, food-drink, games, graphics-design, health-fitness, lifestyle, magazines-newspapers, medical, music, navigation, news, photo-video, productivity, reference, shopping, social-networking, sports, stickers, travel, utilities, weather. Omit for the overall chart.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
metaYes
chartYes
entriesYes
sourcesYes
categoryNo
Behavior5/5

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description adds critical context: the tool relies on an undocumented feed and is best-effort. This discloses potential flakiness not captured by 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: first states the action, second adds a crucial reliability caveat. No extraneous words, and the key information is front-loaded.

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

Completeness5/5

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

Given the presence of an output schema (not shown but noted), the description covers what the tool does, its parameters, and a behavioral caveat. It is sufficient for an agent to understand and invoke the tool correctly.

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 description adds little meaning beyond the input schema, which already has 100% coverage with clear descriptions for all 4 parameters. The tool's overall purpose is stated, but parameter details are not enhanced.

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 uses a specific verb ('Fetch') and clear noun ('ranked top-chart apps') and identifies the scope ('for a storefront'), clearly distinguishing it from sibling tools that handle individual apps, reviews, or search.

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?

The description states the data is 'best-effort' and from an 'undocumented Apple RSS feed', which advises agents to expect possible unreliability. However, it does not explicitly say when to use this instead of alternatives like search_app_store.

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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