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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.

9,443 servers. Last updated -

Matching MCP tools:

Matching MCP servers:

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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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    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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    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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    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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    MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution
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    MIT License
    • Apple

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    A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.
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    An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.
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    An MCP server that provides detailed information about your development environment to the Cursor code editor, enabling more context-aware assistance.
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    Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.
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    Provides comprehensive system diagnostics and hardware analysis through 10 specialized tools for troubleshooting and environment monitoring. Offers targeted information gathering for CPU, memory, network, storage, processes, and security analysis across Windows, macOS, and Linux platforms.
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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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    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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    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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    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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    An MCP server that provides real-time delay information for JR East train lines, accessible via MCP clients like Claude Desktop through the 'getDelays' tool.
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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 track and manage product development projects through structured 7-phase lifecycles with sprint tracking, role-based collaboration, and multi-project support. Provides phase management, progress tracking, and team coordination tools for complete product development workflows.
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    An AI-powered development toolkit for Cursor providing intelligent coding assistance through advanced reasoning, UI screenshot analysis, and code review tools.
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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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    An MCP server that enables AI-powered IDEs to implement a structured development workflow from requirements gathering to code implementation, guiding users through goal collection, requirements specification, design documentation, task planning, and execution.
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    A Model Context Protocol server that enables fetching and processing images from URLs, local file paths, and numpy arrays, returning them as base64-encoded strings with proper MIME types.
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    A Model Context Protocol server that enables searching YouTube videos, retrieving and storing transcripts, and performing semantic search over video content without using the official YouTube API.
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    A Cursor-compatible toolkit that provides intelligent coding assistance through custom AI tools for code architecture planning, screenshot analysis, code review, and file reading capabilities.
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    Enables AI tools to interact with your browser for enhanced frontend development, providing context-rich capabilities like API call analysis, screenshot capture, element inspection, and API testing with automatic authentication.
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    Enables AI tools to interact with browsers for enhanced frontend development, providing context to LLMs through tools like API call analysis, screenshots, element selection, and documentation ingestion.
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    Enforces disciplined programming practices by requiring AI assistants to audit their work and produce verified outputs at each phase of development, following structured workflows for refactoring, feature development, and testing.
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