xMCP acts as a versatile bridge for AI tools, making it much easier for different software applications to talk to each other. Often, AI helper tools require specific programs to be installed directly on a computer to function, which can be a major hurdle if those programs are missing or difficult to set up. This server simplifies the process by allowing these tools to run through a web-based connection, essentially letting an AI use powerful software without needing everything installed locally. Technically, this project is a streamable HTTP proxy designed for MCP servers that typically communicate via standard input/output (stdio). It solves the common "missing dependency" problem by operating within a pre-configured container environment. This container comes ready-to-use with essential development runtimes like `npx`, `bunx`, `uv`, and `docker`, ensuring that the specific commands required by various MCP tools are always available and ready to execute in a stable environment. For developers building sophisticated AI-integrated systems, xMCP provides a consistent and portable command environment that moves tool execution off the local machine and into a controlled workspace. By transforming a local command-line instruction into a structured HTTP request, it enables remote access to tools that were previously restricted to a single computer. This architecture not only streamlines dependency management but also includes a built-in inspector for real-time debugging, giving developers full visibility into how their AI agents are interacting with the tools at their disposal.
Category: AI & LLM Tooling
Tags: containerization, deployment, http, proxy, stdio
bash docker compose up -d Using Make: bash make start The server will be running on http://localhost:3001/mcp.json { "mcpServers": { "context7": { "command": "npx", "args": ["-y", "@upstash/context7-mcp"] } } } xMCP Proxied Configuration: To use the above server via xMCP, use the following URL format: http://localhost:3001/mcp?name=context7&command=npx&args=-y,@upstash/context7-mcp Monitoring & Inspection: * Inspector URL: http://localhost:6274/ * Authentication: The inspector requires a token. To retrieve the URL with the auth token, run: bash make logsnpx * bunx * uv * dockernpx), Python (uv), or Bun (bunx) to be installed and configured in the system PATH. If you are using a locked-down work laptop, a brand-new machine, or a lightweight environment where you don't want to manage multiple programming language runtimes, these tools simply won't work. Solution: xMCP acts as a pre-configured execution environment. Since the xMCP container already has npx, bunx, and uv installed, you can run these MCP servers without installing a single runtime on your host machine. You simply point your AI client to the xMCP HTTP endpoint. Example: To use the Upstash context search tool without having Node.js installed locally, you would configure your Claude Desktop to point to `http:/localhost:3001/mcp?name=upstash&command=npx&args=-y,@upstash/context7-mcpinstead of trying to run thenpx` command directly.
stdio paths on every individual's machine leads to "it works on my machine" errors. Solution: A team can host a single instance of xMCP on a shared internal server. Because xMCP converts local stdio requests into HTTP requests, every developer can connect their AI assistant (Claude, Cursor, etc.) to the same remote HTTP URL. Example: The DevOps team hosts an xMCP instance on a private server. Developers add `http:/internal-mcp-server:3001/mcp?name=k8s-tool&command=kubectl-mcp` to their configurations. Now, everyone uses the same version of the tool without needing to install the tool's specific binary or dependencies locally.
stdio MCPs). This restricts users to only using expensive, hosted "Remote MCPs." Solution: xMCP bridges this gap by acting as a local or remote HTTP gateway. It takes the "command" you want to run, executes it inside its own containerized shell, and streams the output back over HTTP, effectively turning any local stdio server into a remote-accessible web service. Example: You want to use a specialized Python-based data analysis MCP on a tablet-based AI client that only supports HTTP MCP connections. You run xMCP on your home…Part of MCP Servers
xMCP Server is a streamable HTTP proxy that runs stdio MCP servers inside a pre-configured container. It allows AI clients to trigger command-line tools over the network without needing those command-line runtimes installed on the client machine.
The xMCP container comes preinstalled with npx, bunx, uv, and docker. This setup allows users to run typical Node.js, Bun, and Python packages as well as Docker-dependent tasks directly within the container.
You can install and launch xMCP Server locally using Docker Compose or Make. Run docker compose up -d or make start in the cloned project directory to start the service at localhost on port 3001.
The server provides an inspector interface on port 6274. Because the inspector requires an authentication token, execute make logs in your terminal to find the exact inspection URL containing your token.
Yes, xMCP Server is an open-source project. You can inspect the source code, build custom containers with additional tools, and contribute modifications via its GitHub repository.