ASO MCP Server

App Store Optimization toolkit for AI assistants. Keyword research, competitor analysis, review sentiment, metadata optimization — all through the Model Context Protocol.

No API key required. Works out of the box with real App Store data. Supports 155+ countries.

Quick Start

npx aso-mcp

Or install globally:

npm install -g aso-mcp

Why aso-mcp?

• 18 specialized ASO tools — from keyword discovery to App Store Connect metadata management
• Real App Store data — live search results, ratings, reviews, and suggestions
• Custom scoring engine — proprietary algorithm independent of Apple Search Ads API issues
• No API key needed — zero configuration, install and go
• Smart caching — SQLite-backed cache for fast repeated queries
• Rate limiting — built-in request management to avoid Apple throttling
• Multi-country — analyze keywords across 155+ App Store markets

Integration

Claude Desktop

Add to your config file:

OS	Path
macOS	~/Library/Application Support/Claude/claude_desktop_config.json
Windows	%APPDATA%\Claude\claude_desktop_config.json
Linux	~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "aso-mcp": {
      "command": "aso-mcp"
    }
  }
}

Running from source instead?

{
  "mcpServers": {
    "aso-mcp": {
      "command": "npx",
      "args": ["tsx", "/ABSOLUTE/PATH/TO/aso-mcp/src/server.ts"],
      "cwd": "/ABSOLUTE/PATH/TO/aso-mcp"
    }
  }
}

Claude Code

claude mcp add -s user aso-mcp -- npx aso-mcp

Other MCP Clients

Any MCP-compatible client (ChatGPT, Cursor, Windsurf, etc.) can connect via stdio transport. Point it to the aso-mcp command.

Tools

Phase 1 — Keyword Research

Tool	Description
search_keywords	Traffic/difficulty scores + top-ranking apps for a keyword
suggest_keywords	Keyword suggestions by app ID (category, similar, competition strategies)
get_app_details	Full ASO info for an app + metadata analysis

Phase 2 — Competitor Analysis & Optimization

Tool	Description
analyze_competitors	Metadata comparison of top apps for a keyword + keyword gap
optimize_metadata	Title/subtitle/keyword field suggestions with character limit checks
analyze_reviews	Sentiment analysis, complaint and feature request extraction
track_ranking	App's ranking position across multiple keywords
keyword_gap	Keyword difference between two apps + opportunity analysis

Phase 3 — Localization & Reporting

Tool	Description
localized_keywords	Keyword performance comparison across different countries
get_aso_report	Comprehensive ASO report: scores + competitors + reviews in one call

Phase 4 — ASO Generation

Tool	Description
discover_keywords	Keyword discovery from scratch for a new app
generate_aso_brief	Complete ASO brief with keyword pool, competitor patterns, and metadata suggestions

Phase 5 — App Store Connect

Directly read and update your app's metadata on App Store Connect without leaving the AI assistant.

Tool	Description
connect_setup	Configure & validate App Store Connect API credentials
connect_get_app	Find app by bundle ID, get ASC ID + version status
connect_get_metadata	Read current metadata (title, subtitle, keywords, description) for a locale
connect_update_metadata	Update metadata with character limit validation + before/after diff
connect_list_localizations	List all locales and metadata completeness status

App Store Connect Setup

Requires an App Store Connect API Key:

Option A — Environment variables:

export ASC_ISSUER_ID="your-issuer-id"
export ASC_KEY_ID="your-key-id"
export ASC_PRIVATE_KEY_PATH="/path/to/AuthKey_XXXXX.p8"

Option B — Use the setup tool:

"Set up App Store Connect with issuer ID xxx, key ID yyy, and key at /path/to/AuthKey.p8"

Credentials are saved to ~/.aso-mcp/connect-config.json for future sessions.

Utility

Tool	Description
clear_cache	Clears the local data cache for fresh App Store results

Usage Examples

Just ask your AI assistant naturally:

"How competitive is the 'fitness' keyword in the US?"

"Analyze Spotify's competitors and find keyword opportunities"

"Generate an ASO report for com.spotify.client"

"Compare 'music' and 'podcast' keywords across US, UK, and DE markets"

"Do a keyword gap analysis: Spotify vs Apple Music"

"Analyze Shazam's user reviews"

"Suggest title and subtitle for my fitness app targeting: workout, training, exercise"

"Discover keywords for a new calorie tracking app"

"Update my app's subtitle to 'AI Workout Planner' on App Store Connect"

"Show all locales and metadata status for my app"

Scoring Algorithm

The server calculates its own scores, independent of Apple Search Ads API:

Score	Description
Visibility	Based on rating, review count, and ranking position
Competitive	Difficulty derived from the strength of top-ranking apps
Opportunity	High traffic + low difficulty = high opportunity
Overall	Weighted combination of all scores (0-10)

When the aso npm package fails to reach Apple (503 errors), the server automatically falls back to custom scoring using search result analysis — so scores are always available.

Development

git clone https://github.com/kenanatmaca/aso-mcp.git
cd aso-mcp
npm install

npm run dev          # Run with tsx (development)
npm run build        # Compile TypeScript
npm run inspect      # MCP Inspector UI

# Tests
npx tsx test.ts              # Core tests (17)
npx tsx test-phase3.ts       # Localization & report tests (4)
npx tsx test-generation.ts   # ASO generation tests (8)

Tech Stack

• TypeScript + Node.js 22+
• MCP SDK — Model Context Protocol
• app-store-scraper — App Store data
• aso — ASO scoring with automatic fallback
• better-sqlite3 — Cache layer
• Zod — Schema validation

License

MIT

Author

Kenan Atmaca