App Store Connect MCP Server

# PRFAQ: App Store Connect MCP Server **By Trial and Error Inc** **Date: August 21, 2025** **Author: Sid Sarasvati** **Status: Draft v1.0** --- ## PRESS RELEASE ### Trial and Error Inc Launches Open-Source App Store Connect MCP Server, Eliminating iOS Metrics Integration Headaches for AI-Powered Development **BOSTON, MA - August 21, 2025** - Trial and Error Inc today announced the release of `appstore-connect-mcp`, an open-source Model Context Protocol (MCP) server that gives AI assistants like Claude direct access to App Store Connect data. This eliminates the manual export-import cycle that has plagued iOS developers trying to leverage AI for business intelligence and app management. The server solves a critical problem: while iOS apps generate rich analytics and financial data in App Store Connect, getting this data into AI workflows has required manual exports, screenshots, or broken third-party packages. With appstore-connect-mcp, developers can simply ask their AI assistant questions like "What's our iOS monthly recurring revenue?" or "Show me crash rates for the last version" and receive instant, accurate answers. "We built this because we were tired of npm packages that didn't exist and GitHub repos that didn't build," said Sid Sarasvati, founder of Trial and Error Inc. "When we needed iOS metrics for RenovateAI and found the 'official' MCP server returned 404 errors, we decided to build the solution ourselves. Three hours later, we had working code. Now we're sharing it with everyone." The server provides comprehensive access to: - **Financial Metrics**: Sales reports, subscription data, regional breakdowns - **User Analytics**: Active users, retention, engagement metrics - **App Health**: Crash reports, performance data, user reviews - **Beta Testing**: TestFlight user counts, feedback, build distribution Unlike existing solutions, appstore-connect-mcp: - Works immediately with zero configuration beyond API credentials - Handles authentication, pagination, and rate limiting automatically - Provides formatted responses optimized for AI consumption - Runs locally for maximum security and privacy The server integrates seamlessly with Claude Desktop, Cursor, and any MCP-compatible AI tool. Setup takes less than 5 minutes with an App Store Connect API key. "This changes how we think about app metrics," added Sarasvati. "Instead of logging into dashboards and exporting CSVs, we just ask Claude. It's like having a data analyst who never sleeps and knows every metric instantly." The project is available immediately on GitHub at github.com/trialanderrorinc/appstore-connect-mcp under the MIT license. Trial and Error Inc plans to maintain the project actively and welcomes community contributions. Companies using the server can expect: - 90% reduction in time spent gathering iOS metrics - Instant access to financial data for board reports - Real-time monitoring of app health metrics - Automated competitive analysis capabilities The release comes as Model Context Protocol adoption accelerates, with companies like Anthropic, Block, and Apollo integrating MCP into their workflows. This server extends that ecosystem to the $200 billion iOS app economy. For more information, visit github.com/trialanderrorinc/appstore-connect-mcp --- ## FREQUENTLY ASKED QUESTIONS ### External FAQ (Customer-Facing) **Q: What is the App Store Connect MCP Server?** A: It's a bridge that connects AI assistants like Claude to your App Store Connect data, enabling natural language queries about your iOS app's metrics, finances, and user data without manual exports or API coding. **Q: Who needs this?** A: Any iOS developer or company that: - Needs regular financial reports from App Store data - Wants to monitor app metrics without dashboard fatigue - Uses AI assistants for development or business intelligence - Is tired of manual CSV exports and data manipulation - Has investors asking for iOS-specific metrics **Q: How is this different from Apple's App Store Connect API?** A: Apple's API requires writing code, handling authentication, parsing responses, and building your own tools. Our MCP server handles all of this and presents it in a format AI assistants understand. You talk to Claude; Claude talks to our server; our server talks to Apple. **Q: What AI assistants does this work with?** A: Any tool supporting Model Context Protocol (MCP): - Claude Desktop (primary) - Cursor - Continue.dev - Any custom MCP client - Future MCP-compatible tools **Q: How long does setup take?** A: 5 minutes if you already have App Store Connect API credentials. 15 minutes if you need to generate them. Compare this to days or weeks building custom integrations. **Q: Is this secure?** A: Yes. The server runs locally on your machine, your API credentials never leave your environment, and all communication with Apple uses their official secure API. We never see your data. **Q: What metrics can I access?** A: Everything available through App Store Connect API v3.2: - Financial reports (sales, proceeds, subscriptions) - Analytics (users, sessions, retention, engagement) - App information (versions, metadata, pricing) - Beta testing (TestFlight users, builds, feedback) - Reviews and ratings - Crash and performance data **Q: Does this work for multiple apps?** A: Yes. One server instance can access all apps associated with your App Store Connect account. **Q: What does it cost?** A: Free. MIT licensed open-source. Use it commercially, modify it, distribute it. **Q: Why did Trial and Error Inc build this?** A: We needed it for RenovateAI (90% of revenue is iOS). Existing solutions were broken or didn't exist. We built it in 3 hours and realized everyone has this problem. --- ## INTERNAL FAQ (Technical) **Q: Why MCP instead of a REST API or GraphQL?** A: MCP is purpose-built for AI assistant integration. REST/GraphQL would require another translation layer. MCP provides direct tool calling, automatic documentation, and standardized error handling that AI models understand natively. **Q: What's the technical architecture?** A: Three layers: 1. **Authentication Layer**: JWT generation from P8 keys, token refresh 2. **API Client Layer**: Typed wrappers around App Store Connect endpoints 3. **MCP Server Layer**: Tool definitions, response formatting, error handling TypeScript throughout for type safety. Minimal dependencies (MCP SDK, jsonwebtoken, axios). **Q: How do we handle Apple's rate limits?** A: - Exponential backoff with jitter - Request queuing and batching where possible - Token caching (20-minute validity) - Configurable rate limit thresholds - Graceful degradation with partial data on limits **Q: What about pagination for large datasets?** A: Automatic pagination with configurable limits. For AI contexts, we default to reasonable chunks (100 items) with summaries. Full dataset access available via specific tool parameters. **Q: How do we handle different API versions?** A: Currently targeting App Store Connect API v3.2. Version detection on startup with compatibility warnings. Abstraction layer allows version-specific handlers without breaking changes. **Q: What's the testing strategy?** A: - Unit tests for auth and API clients - Integration tests with mock Apple responses - End-to-end tests with real credentials (in CI) - Snapshot testing for MCP response formats - Property-based testing for pagination logic **Q: How do we ensure reliability?** A: - Comprehensive error handling with fallbacks - Detailed logging (configurable levels) - Health check endpoints - Automatic retry logic - Circuit breaker pattern for API failures **Q: What's the deployment model?** A: - Local-first (runs on developer machines) - Docker support for containerized deployment - Optional cloud deployment (user-managed) - No SaaS offering (privacy/security focused) **Q: How do we handle authentication secrets?** A: - P8 keys never in config files - Environment variables or file paths only - Optional encryption at rest - Credential validation on startup - Clear error messages for auth issues **Q: What about performance?** A: - Response caching (configurable TTL) - Parallel request execution where safe - Lazy loading of large datasets - Streaming responses for real-time data - Memory-efficient data structures **Q: How do we maintain backwards compatibility?** A: - Semantic versioning strictly followed - Deprecation warnings for breaking changes - Migration guides for major versions - Tool aliasing for renamed functions - Config migration utilities **Q: What metrics do we track?** A: - Usage analytics (opt-in, anonymous) - Error rates and types - Performance metrics (response times) - Popular tool usage patterns - Version adoption rates **Q: What's the support model?** A: - GitHub issues for bug reports - Community Discord for discussions - Quarterly roadmap updates - Security patches within 48 hours - Best-effort response (not SLA-backed) **Q: How do we handle different regions/locales?** A: - Automatic region detection from API - Configurable currency/date formats - Localized error messages (future) - Regional compliance considerations **Q: What about competitive differentiation?** A: - First reliable MCP server for App Store Connect - Focus on AI-optimized responses - Active maintenance vs abandoned repos - Trial and Error Inc brand trust - Community-driven development --- ## SUCCESS METRICS **Launch Success (Week 1)**: - 100+ GitHub stars - 10+ production deployments - 5+ community contributors - Zero critical bugs **Growth Success (Month 1)**: - 1,000+ npm downloads - 50+ GitHub issues (engagement) - 3+ blog posts/tutorials by others - 1+ enterprise adoption **Long-term Success (Year 1)**: - Become default solution for iOS + AI integration - 10,000+ monthly active deployments - Sustainable maintenance model - Expansion to other app stores (Google Play) --- ## RISKS AND MITIGATIONS **Risk**: Apple changes API breaking compatibility **Mitigation**: Version detection, graceful degradation, rapid patches **Risk**: Abandoned like other MCP servers **Mitigation**: Open source, documented architecture, community ownership path **Risk**: Security vulnerabilities **Mitigation**: Security-first design, rapid patching, responsible disclosure **Risk**: Poor adoption **Mitigation**: RenovateAI as reference customer, content marketing, developer evangelism **Risk**: Complexity creep **Mitigation**: Core features only, plugin architecture for extensions --- ## APPENDIX: COMPETITIVE LANDSCAPE **Existing Solutions and Why They Fail**: 1. **@joshuarileydev/app-store-connect-mcp-server** - Status: NPM 404, doesn't exist - Problem: Published to wrong registry or never published 2. **Manual API Integration** - Status: Works but complex - Problem: Days of development, maintenance burden 3. **Dashboard Screenshots** - Status: Common practice - Problem: Manual, error-prone, not real-time 4. **CSV Exports** - Status: Apple's solution - Problem: Manual, no automation, stale data 5. **Third-party Analytics (Amplitude, Mixpanel)** - Status: Partial solution - Problem: Missing financial data, another vendor **Our Advantage**: We're shipping working code, today, with RenovateAI as proof it works. --- *End of PRFAQ v1.0*