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Open-Source MCP servers

Production-ready MCP servers that extend AI capabilities through file access, database connections, API integrations, and other contextual services.

10,362 servers. Last updated -

Matching MCP tools:

Matching MCP servers:

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    Persistent memory and pattern learning for AI coding assistants via MCP.
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    Enables AI assistants to build, test, run, and manage Apple platform projects (iOS, macOS, tvOS, watchOS, visionOS) directly through Xcode. Provides comprehensive control over Xcode projects, Swift packages, simulators, and development workflows without leaving your editor.
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    A local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.
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    Provides programmatic access to Xcode functionality, enabling AI assistants to create, build, test, and manage iOS/macOS projects directly.
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    Bridges Claude AI with Xcode, enabling AI-powered code assistance, project management, and automated development tasks securely on your local machine.
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    A server that acts as a bridge between Claude and local Xcode projects, enabling AI-powered code assistance, project management, and automated development tasks without exposing your code to the internet.
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    A Model Context Protocol server that provides tools for Xcode-related operations, making it easier to work with iOS project management, building, testing, archiving, and deploying apps to both simulators and physical devices.
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    A very simple vector store that provides capability to watch a list of directories, and automatically index all the markdown, html and text files in the directory to a vector store to enhance context.
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    A Model Context Protocol server that enables AI assistants to build and test Xcode projects directly through a standardized interface, with capabilities for running tests, monitoring progress, and accessing logs in real-time.
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    Enable Claude Code, Cursor, or your favorite LLM to interact with Xcode, building your projects the same way you do, and seeing the same errors. Greatly increases productivity when working on iOS, iPadOS, macOS, visionOS, tvOS projects & Swift packages - or any time you might use Xcode.
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    Connects to Xcode's build system to extract, parse, and display errors and warnings from your Swift projects, helping AI assistants quickly identify code issues without manually searching through build logs.
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    All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...
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    An all-in-one development toolkit that integrates 10 popular MCPs with comprehensive features for solo developers, supporting frontend, backend, mobile development, database management, testing, and deployment automation.
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    Gives Goose/Cursor access to your iOS/macOS project index through the Model Control Protocol (MCP) and IndexStoreDB. This provides exhaustive lists of function call sites to help your agent with refactoring and code navigation.
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    Apache 2.0
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    Provides comprehensive development tools for FastMCP projects including documentation access, NPM package version management, and TypeScript type definitions retrieval. Enables developers to fetch FastMCP documentation, analyze NPM packages, and access MCP architecture information through natural language.
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    A Model Context Protocol server that enables seamless integration between Claude AI and development tools like VSCode, Augment, Vercel, Airtable, and Square.
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    Facilitates spec-driven development workflows by providing structured prompts for generating requirements in EARS format, design documents, and implementation code following a systematic approach.
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    A comprehensive toolkit that consolidates over 40 development tools into a single MCP, enabling unified management of frontend, backend, mobile development, GitHub repositories, and database operations.
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    A unified control center for managing MCP servers, providing tooling for environment variable management, profile-based configurations, and local package installation automation.
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    A testing environment for MCP server setup and client-server interactions, allowing users to verify basic MCP server functionality and test client-server communications including shell command execution.
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    An MCP server that automatically generates static websites from natural language instructions, creating Next.js-based sites with optional automatic deployment to AWS S3.
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    A Model Context Protocol server that enables Claude to manage software development projects with complete context awareness and code execution through Docker environments.
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    Problem: MCP clients can't efficiently use Xcode CLI tools because the build and simulator listing commands return more than 50k tokens, exceeding MCP limits. Solution: MCP tooling using progressive disclosure with intelligent caching.
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    Provides comprehensive access to Salesforce B2C Commerce Cloud development tools including SFCC API documentation, best practices guides, log analysis, and system object definitions. Enables AI assistants to help with SFCC development tasks through both documentation-only mode and full credential-based mode.
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    A powerful Model Context Protocol framework that extends Cursor IDE with tools for web content retrieval, PDF processing, and Word document parsing.
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    A TypeScript scaffold for building Model Context Protocol (MCP) tools that let AI models call external services like sending emails or notifications.
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    Enables automated generation of FreeRTOS embedded projects for chips like BK7252, STM32F4, and ESP32. Provides intelligent project structure creation, peripheral driver code generation, and complete embedded development workflows through natural language interaction.
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    A comprehensive framework for building reliable Python MCP servers that enforces incremental development practices through Cursor Rules, featuring built-in security measures and a smart rule system.
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    Enables AI-guided spec-driven development workflow that transforms ideas into implementation through structured stages: goal collection, requirements gathering in EARS format, technical design documentation, task planning, and systematic code execution.
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    Enables developers to access kintone API specifications, field type documentation, and development best practices through natural language queries. Supports API request validation and provides comprehensive development guidance for kintone customizations.
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    Enables AI agents to interact with Magento 2 development environments through comprehensive tools for module management, database operations, cache control, configuration management, and system diagnostics. Supports complete development workflows from module creation to deployment and troubleshooting.
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