Open Source Windows Model Context Protocol (MCP) Servers
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Model Context Protocol (MCP) Servers for Windows
View 1868 business solutionsBrowse free open source Model Context Protocol (MCP) Servers and projects for Windows below. Use the toggles on the left to filter open source Model Context Protocol (MCP) Servers by OS, license, language, programming language, and project status.
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xiaohongshu-mcp
MCP for xiaohongshu.com
xiaohongshu-mcp is a Model Context Protocol (MCP) server that equips AI assistants with first-class tools for working on Xiaohongshu (Little Red Book), focusing on day-to-day creator and operator workflows rather than generic browsing. The project centers on authenticated actions and data access that matter to content operations, such as checking login state, publishing or scheduling content, fetching recommendations and search results, reading post details, and acting on comments. It’s packaged so MCP-capable clients (e.g., Claude Desktop, Cursor) can discover its tools via schemas instead of prompt guesswork, which improves reliability and reduces brittle automation. The repo highlights a growing community and provides links to a hosted landing page, signaling that the server is intended for practical use beyond a proof of concept. By exposing typed resources and procedures, it enables repeatable, auditable automation in social workflows where UI changes are frequent. -
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MarkItDown
Python tool for converting files and office documents to Markdown
MarkItDown is a lightweight Python utility developed by Microsoft for converting various files and office documents to Markdown format. It is particularly useful for preparing documents for use with large language models and related text analysis pipelines. -
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ScreenPipe
AI app store powered by 24/7 desktop history. open source
Screenpipe is an AI app store powered by continuous desktop history recording. It operates entirely locally, offering developers a platform to build, distribute, and monetize AI applications that leverage comprehensive contextual data from users' desktop activities. -
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Chrome DevTools MCP
Chrome DevTools for coding agents
chrome-devtools-mcp is an MCP server that connects AI agents to the Chrome DevTools Protocol so they can inspect pages, record traces, read console/network data, and modify the live browser state under user control. It makes a running Chrome instance visible to MCP clients, enabling agents to debug websites end-to-end—launching Chrome, navigating, profiling, and collecting artifacts in a structured way. The repository spells out environment requirements and cautions that exposing a live browser to agents grants powerful access, so sensitive data should be handled carefully. Beyond static inspection, it exposes operational tools like starting a performance trace that an agent can later analyze to propose optimizations. The server is intended to slot into MCP-capable assistants and IDEs, giving them reliable, typed tools and resource endpoints rather than ad-hoc automation. Documentation from the Chrome team explains how the server augments agents with real debugging capabilities. -
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Lemonade
Lemonade helps users run local LLMs with the highest performance
Lemonade is a local LLM runtime that aims to deliver the highest possible performance on your own hardware by auto-configuring state-of-the-art inference engines for both NPUs and GPUs. The project positions itself as a “local LLM server” you can run on laptops and workstations, abstracting away backend differences while giving you a single place to serve and manage models. Its README emphasizes real-world adoption across startups, research groups, and large companies, signaling a focus on practical deployments rather than toy demos. The repository highlights easy onboarding with downloads, docs, and a Discord for support, suggesting an active user community. Messaging centers on squeezing maximum throughput/latency from modern accelerators without users having to hand-tune kernels or flags. Releases further reinforce the “server” framing, pointing developers toward a service that can be integrated into apps and tools. -
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Ghidra MCP
Socket based MCP Server for Ghidra
GhidraMCP is a Ghidra plugin implementing the Model Context Protocol, bridging Ghidra's reverse engineering capabilities with AI assistants to enhance binary analysis tasks. -
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IDA Pro MCP
MCP Server for IDA Pro
The IDA Pro MCP Server is a Model Context Protocol (MCP) server designed to integrate with IDA Pro, a popular disassembler and debugger. It enables AI assistants to interact with IDA Pro, facilitating tasks such as code analysis and reverse engineering. -
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Telegram MCP
MCP server to work with Telegram through MTProto
An MCP server that bridges the Telegram API and AI assistants, enabling seamless interaction between AI applications and Telegram through MTProto. -
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HexStrike AI MCP Agents
HexStrike AI MCP Agents is an advanced MCP server
HexStrike AI is an MCP server that lets LLM agents autonomously operate a large catalog of offensive-security tools. Its goal is to bridge “language models” and practical pentest workflows—enumeration, exploitation, vulnerability discovery, and bug bounty reconnaissance—under safe, auditable controls. The server exposes typed tools and guardrails so agent prompts translate to concrete, parameterized actions rather than brittle shell strings. It ships with curated tool adapters, task orchestration, and guidance for connecting popular agent clients (Claude, GPT, Copilot) to a hardened execution environment. Documentation highlights the breadth of supported utilities and positions HexStrike as a research and red-team aid, not a point-and-click exploit kit. A public site and active repository activity signal an expanding community around autonomous security research agents. -
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Context7 Platform
Up-to-date code documentation for LLMs and AI code editors
Context7 is a system that aims to inject fresh, version-specific documentation and code snippets into language model prompts, thereby avoiding reliance on outdated training data or hallucinated APIs. It’s designed to integrate with tools that support the Model Context Protocol (MCP), such as Cursor, Windsurf, and other LLM clients. When a user writes a prompt and appends something like “use context7,” the system detects the libraries or frameworks being asked about, fetches the latest docs/snippets from the source repositories, filters and packages relevant context, and injects them into the LLM’s prompt to guide it toward accurate, up-to-date code. The upstream codebase provides an MCP server implementation, enabling clients to easily interface with the Context7 service over standard channels (HTTP, stdio) and treat it as an external “knowledge tool.” -
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Firebase MCP
Model Context Protocol (MCP) server to interact with Firebase service
A Model Context Protocol (MCP) server that enables Large Language Model (LLM) clients to interact seamlessly with Firebase services, facilitating operations across Authentication, Firestore, and Storage. -
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MCP Atlassian
MCP server that integrates Confluence and Jira
The MCP Atlassian server integrates Atlassian products like Confluence and Jira with the Model Context Protocol. It supports both Cloud and Server/Data Center deployments, enabling AI models to interact with these platforms securely. -
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XHS-Downloader
GUI/CLI tool for downloading Xiaohongshu
XHS-Downloader is a GUI/CLI tool for downloading Xiaohongshu (Little Red Book) content without watermarks, supporting both graphics and video posts. Prebuilt packages for Windows and macOS are available from Releases and GitHub Actions artifacts, so most users can run it by unzipping and launching the included executable. The project offers two execution paths—run the compiled app or run from source—and documents default download and configuration paths to simplify first use. Recent releases add format support like JPEG and HEIC, clipboard-listening mode improvements, author-based archiving, SOCKS/HTTP proxy options, and the ability to set the file’s modification time to the post’s publish time for cleaner library organization. There is an active issues/discussions area with community tips, including approaches that use Selenium to acquire cookies and user agents for more reliable downloads. -
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GitHub MCP Server
GitHub's official MCP Server
The GitHub MCP Server exposes GitHub as a Model Context Protocol server so AI assistants can safely act on repos, issues, pull requests, gists, and more through a consistent tool interface. It’s designed to run locally or remotely and then be attached to MCP-capable clients (for example, Copilot Chat) so an LLM can search code, open files, create branches, draft PRs, label or triage issues, and query metadata without hard-coding GitHub APIs. The server defines tools and resources with fine-grained scopes, leaning on GitHub’s auth to enforce least privilege and auditable access. It supports both stdio and HTTP transports, enabling IDE and headless integrations, and adopts common MCP behaviors like prompts, schemas, and tool definitions to keep agent calls predictable. Documentation covers setup, tokens, and client configuration, highlighting native editor integrations. Its design goal is to give AI agents first-class, governed access to GitHub workflows. -
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Binary Ninja MCP
A Binary Ninja plugin, MCP server
The Binary Ninja MCP is a plugin and bridge that integrates Binary Ninja with Large Language Model clients via the Model Context Protocol, enhancing reverse engineering workflows with AI assistance. -
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Browser Use MCP Server
Browse the web, directly from Cursor etc.
A browser automation server implementing the Model Context Protocol, designed to allow AI assistants to browse the web directly from applications like Cursor. It supports natural language commands for web navigation and interaction. -
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Claude-Flow
The leading agent orchestration platform for Claude
Claude-Flow v2 Alpha is an advanced AI orchestration and automation framework designed for enterprise-grade, large-scale AI-driven development. It enables developers to coordinate multiple specialized AI agents in real time through a hive-mind architecture, combining swarm intelligence, neural reasoning, and a powerful set of 87 Modular Control Protocol (MCP) tools. The platform supports both quick swarm tasks and persistent multi-agent sessions known as hives, facilitating distributed AI collaboration with persistent contextual memory. At its core, Claude-Flow integrates Dynamic Agent Architecture (DAA) for self-organizing agent management, neural pattern recognition accelerated by WebAssembly SIMD, and a SQLite-based memory system for context retention and knowledge persistence across tasks. It automates development workflows via pre- and post-operation hooks, providing seamless coordination, code formatting, validation, and performance optimization. -
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FileScope MCP
Analyzes your codebase identifying important files based on dependency
FileScopeMCP is a TypeScript-based tool that analyzes codebases to identify important files based on dependency relationships. It generates diagrams and importance scores per file, aiding in understanding code structure and assisting AI assistants in navigating complex projects. -
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Jira MCP
A Go-based MCP (Model Control Protocol) connector for Jira
The Jira MCP is a Go-based MCP connector that enables AI assistants to interact with Atlassian Jira. It provides a seamless interface for performing common Jira operations, including issue management, sprint planning, and workflow transitions. -
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MCP Notion Server
MCP Server for the Notion API, enabling Claude to interact with Notion
The MCP Notion Server is an MCP server implementation that integrates with Notion, allowing AI assistants to interact with and manage Notion content. It facilitates seamless access to Notion's databases and pages. -
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MCP Server Playwright
MCP server for browser automation using Playwright
An MCP (Model Context Protocol) server that leverages Playwright to provide browser automation capabilities, enabling large language models (LLMs) to interact with web pages, take screenshots, and execute JavaScript within a real browser environment. -
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MySQL MCP Server
A Model Context Protocol (MCP) server that enables secure interaction
The MySQL MCP Server enables secure interaction with MySQL databases, allowing AI assistants to list tables, read data, and execute SQL queries through a controlled interface. It is designed for integration with AI applications like Claude Desktop and should not be run as a standalone Python program. -
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Playwright MCP
Playwright MCP server
An MCP server developed by Microsoft that offers browser automation capabilities using Playwright, enabling LLMs to interact with web pages through structured accessibility snapshots without relying on visual data. -
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web-eval-agent MCP Server
An MCP server that autonomously evaluates web applications
web-eval-agent is a Model Context Protocol (MCP) server that spins up a browser-use–capable debugging agent to autonomously run and evaluate web apps straight from your editor. It’s positioned as a “let the coding agent debug itself” companion: the agent launches the app, navigates flows, captures evidence, and iterates on failures without manual copy-pasting of logs. The repository focuses on developer ergonomics, exposing typed MCP tools so clients like Claude Desktop can start sessions, gather traces, and reason over failures with structured artifacts. Marketing and README material emphasize supercharging local debugging loops by combining live browser execution with LLM-driven hypotheses and fixes. Activity on the repo shows steady iteration, with issues and PRs centered on reliability and developer experience. In short, it wraps autonomous, in-editor web testing and diagnosis behind a predictable MCP interface. -
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Colab-MCP
An MCP server for interacting with Google Colab
Colab-MCP is an open-source Model Context Protocol server developed by Google that enables AI agents to directly interact with and control Google Colab environments programmatically, transforming Colab into a fully automated, agent-accessible workspace. Instead of relying on manual notebook usage, the system allows MCP-compatible agents to execute code, manage files, install dependencies, and orchestrate entire development workflows within Colab’s cloud infrastructure. This approach bridges the gap between local AI agents and remote high-performance compute environments, allowing users to offload heavy workloads such as machine learning training, data analysis, and dependency-heavy tasks to Colab’s GPU and TPU resources. By exposing Colab as an MCP server, the tool enables seamless integration with a wide range of AI assistants and agent frameworks, creating a standardized interface for tool use and execution.
