MCP Servers

Official Apple Xcode MCP server (xcrun mcpbridge) that gives external AI agents direct access to Xcode IDE capabilities. Provides 20 native tools for building projects, running tests, reading and writing files in the project navigator, searching code with regex, rendering SwiftUI previews, executing code snippets, browsing Apple Developer documentation, and inspecting build logs and workspace issues. Requires Xcode 26+ with MCP enabled in Intelligence settings.

Official Expo MCP server that connects AI coding assistants to Expo projects and EAS services. Enables searching and reading Expo documentation, managing EAS builds and workflows, installing compatible libraries, inspecting TestFlight crashes and feedback, and automating visual verification through simulator screenshots and interactions. Supports both remote server capabilities and local development server features for advanced automation.

Provides persistent memory capabilities through a knowledge graph stored in a local JSON file. Enables AI agents to create, read, update, and delete entities and their relationships. Useful for maintaining context across conversations, storing user preferences, and building structured knowledge bases that persist between sessions.

Official MCP server for the Stripe API. Enables AI agents to interact with Stripe's payment infrastructure including creating and managing customers, payment intents, subscriptions, invoices, and products. Supports reading transaction data, handling refunds, and querying balance information. Useful for building payment integrations, debugging billing issues, and automating financial operations.

MCP server for Grafana's observability platform. Enables AI agents to query metrics from Prometheus, search and analyze logs from Loki, query traces, list and manage dashboards, and investigate incidents. Useful for debugging production issues, building monitoring dashboards, and performing root cause analysis with AI assistance across the full Grafana LGTM stack.

Official MCP server for Qdrant vector search engine. Acts as a semantic memory layer enabling AI agents to store and retrieve information using vector similarity search. Supports storing text with metadata, semantic querying, configurable embedding models via FastEmbed, and both cloud-hosted and local Qdrant instances. Useful for building RAG pipelines, code search, knowledge bases, and long-term agent memory.