Open Source Windows Model Context Protocol (MCP) Servers
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Model Context Protocol (MCP) Servers for Windows
View 1888 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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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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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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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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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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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. -
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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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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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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PPTAgent
PPTAgent: Generating and Evaluating Presentations
PPTAgent is a research system for generating and evaluating slide decks that goes beyond simple text-to-slides. It follows a two-stage, edit-based workflow: first it analyzes reference presentations to infer slide roles and structure, then it drafts an outline and iteratively performs editing actions to produce new slides. The project includes both the generation agent and an evaluation framework, PPTEval, to score content quality, design, and coherence. The repository highlights the EMNLP 2025 paper and provides links to resources for replication and study. The approach reflects human presentation practice—plan, draft, then refine with edits—yielding more coherent decks than direct one-shot generation. Community interest and stars suggest strong uptake for research and tooling around presentation automation. -
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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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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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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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AWS MCP Servers
Helping you get the most out of AWS, wherever you use MCP
AWS MCP Servers are a collection of remotely hosted, fully-managed Model Context Protocol (MCP) servers by AWS, providing AI applications with real-time access to AWS documentation, API references, best practices, and infrastructure-management capabilities via natural-language workflows. An MCP Server is a lightweight program that exposes specific capabilities through the standardized Model Context Protocol. Host applications (such as chatbots, IDEs, and other AI tools) have MCP clients that maintain 1:1 connections with MCP servers. Common MCP clients include agentic AI coding assistants (like Q Developer, Cline, Cursor, Windsurf) as well as chatbot applications like Claude Desktop, with more clients coming soon. MCP servers can access local data sources and remote services to provide additional context that improves the generated outputs from the models. -
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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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n8n-MCP
A MCP for Claude Desktop / Claude Code / Windsurf / Cursor
n8n-mcp is a Model Context Protocol (MCP) server that turns the n8n workflow platform into a set of first-class, typed tools an AI assistant can understand and operate. It exposes structured knowledge of n8n nodes and operations so an agent can reason about workflows, parameters, and executions without scraping docs or guessing API shapes. The server focuses on making Claude Desktop (and other MCP-capable clients) “n8n-literate,” enabling tasks such as inspecting existing workflows, proposing node chains, and validating configuration before runs. It ships with organized resources and tool definitions that map cleanly to n8n’s ecosystem, improving reliability compared with ad-hoc prompt patterns. The project targets practical agent ops: safer mutations, better error reporting, and predictable behavior when automating or refactoring automations. Community posts highlight the goal of giving agents accurate knowledge of hundreds of n8n nodes and keeping that knowledge fresh as n8n evolves. -
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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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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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MCP Bridge
A middleware to provide an openAI compatible endpoint
MCP-Bridge serves as a middleware that connects the OpenAI API with MCP tools, allowing developers to utilize MCP functionalities through the OpenAI API interface. It provides endpoints compatible with OpenAI, facilitating seamless integration and enabling the use of MCP tools without requiring explicit MCP support in clients. -
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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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MCP Linear
MCP server that enables AI assistants to interact with Linear project
The MCP Linear server is a Model Context Protocol (MCP) implementation that enables AI assistants to interact with the Linear project management system through natural language. It allows users to retrieve, create, and update issues, projects, and teams within Linear, facilitating seamless integration between AI models and project management workflows. -
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Wren Engine
The Semantic Engine for Model Context Protocol(MCP)
Wren Engine is a semantic engine designed to empower Model Context Protocol (MCP) clients and AI agents by providing accurate, contextual, and governed access to business data. It serves as a bridge between large language models (LLMs) and enterprise systems, facilitating seamless integration and interaction. -
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Last9 MCP Server
Last9 MCP Server
The Last9 MCP Server is a Model Context Protocol server implementation for Last9, enabling AI agents to seamlessly bring real-time production context—logs, metrics, and traces—into local environments to auto-fix code faster. -
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MCP Claude Code
MCP implementation of Claude Code capabilities and more
The MCP Claude Code server implements Claude Code-like functionality, allowing AI assistants to directly execute instructions for modifying and improving project files. It leverages the Model Context Protocol for seamless integration with various MCP clients. -
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MCP Filesystem Server
Go server implementing Model Context Protocol (MCP) for filesystem
Filesystem MCP Server is a Go-based server implementing the Model Context Protocol (MCP) for filesystem operations. It allows for various file and directory manipulations, including reading, writing, moving, and searching files, as well as retrieving file metadata. -
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MCP Server JS
An MCP (Model Context Protocol) server
An MCP (Model Context Protocol) server that enables AI platforms to interact with YepCode's infrastructure, allowing users to run large language model (LLM) generated scripts and transform YepCode processes into powerful tools accessible directly by AI assistants. -
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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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Firecrawl MCP Server
Adds powerful web scraping and search to Cursor and Claude
firecrawl-mcp-server is the official MCP integration for Firecrawl that brings high-recall web scraping, crawling, and search into IDEs and agent runtimes. It exposes tools for single-page scrape, multi-URL batch jobs, site discovery, and search enrichment, returning cleaned, structured content suitable for downstream LLM reasoning. The server is designed to run with Firecrawl’s hosted API or self-hosted deployments, making it flexible for enterprise data-governance requirements. Built-in behaviors include JavaScript rendering, automatic retries, and streamable HTTP so long pages and large crawls can flow incrementally into agents. Because it’s an MCP server, clients get typed inputs/outputs and standardized error handling rather than ad-hoc prompt instructions. The repository is active, widely starred, and includes quick starts that make it easy to add web research to an agent stack.
