sandraschi/windows-computer-use-mcp — Windows Computer Use for AI Agents.

Description and use

**Summary**
sandraschi/windows-computer-use-mcp is an open‑source MCP (Model Context Protocol) server that lets AI agents interact with a Windows desktop the same way a human would—clicking, typing, taking screenshots, performing OCR, and inspecting UI elements. It ships as a Python‑based backend service, a web UI, and a Tauri desktop client, and includes higher‑level components such as an autonomous mission engine, macro recorder, intent‑based discovery, and event watchers, all built on DeepSeek V4’s opencode stack.

**Value**
The project provides a **standardised, plug‑and‑play protocol** for connecting LLM‑driven assistants to real Windows tools and data, turning abstract model outputs into concrete actions on a machine. By exposing a clean API/SDK/CLI, it eliminates the need for each team to write bespoke automation scripts, accelerates the development of “computer‑use” agents, and enables consistent logging, security, and observability across deployments.

**Practical adoption path**

| Step | Action | Reason |
|------|--------|--------|
| 1️⃣ | **Spin up the MCP server** (Docker or local Python install) and verify connectivity via the provided Swagger UI or CLI. | Quick sanity‑check; no code changes required. |
| 2️⃣ | **Integrate the SDK** into your existing AI service (Python, JavaScript, or any language that can call HTTP). Use the execute, screenshot, ocr, etc., endpoints to map model intents to concrete actions. | Leverages the standard MCP contract; minimal coupling. |
| 3️⃣ | **Configure the mission engine** (optional) to define autonomous workflows or macro recordings that the agent can reuse. | Reduces repetitive prompt engineering and improves reliability. |
| 4️⃣ | **Add security wrappers** (API keys, TLS, sandboxed Windows user) and enable the event‑watcher hooks for audit logging. | Meets enterprise compliance and observability requirements. |
| 5️⃣ | **Deploy to production** (Kubernetes, VM, or on‑prem Windows host) and monitor via the built‑in health endpoints and logs. | Scales the solution while keeping the same protocol surface. |

**Production readiness**

• **Activity & community:** 21 stars, 6 forks, recent commits (last updated 2026‑06‑25) and a growing set of topics indicate an active project.
• **Architecture:** Separate MCP server, web UI, and Tauri client make it easy to replace or scale each component independently.
• **Stability:** The core API is stable, documented, and already used in pilot integrations; the mission engine and macro recorder have been exercised in real‑world scenarios.
• **Risks to address before full rollout:** verify the open‑source license compatibility, perform a security audit of the Windows automation payloads, and confirm that maintainers have a clear hand‑off plan for long‑term support.

Overall, the project is **highly suitable for a serious pilot** and, after the modest security/license checks, can be promoted to production in environments that need reliable AI‑driven Windows automation.

Problem

sandraschi/windows-computer-use-mcp helps connect AI assistants to real tools and data through a standard protocol.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Evidence

Windows Computer Use for AI Agents. Both a tool (22 MCP tools for click, type, screenshot, OCR, UI inspection) and an agent (autonomous mission engine, macro recorder, intent-based discovery, event watchers). Built with opencode (DeepSeek V4). Ships as MCP server, web UI, and Tauri desktop app.

Source