1MCP - One MCP Server for All

A unified Model Context Protocol server implementation that aggregates multiple MCP servers into one.

Overview

1MCP (One MCP) is designed to simplify the way you work with AI assistants. Instead of configuring multiple MCP servers for different clients (Claude Desktop, Cherry Studio, Cursor, Roo Code, Claude, etc.), 1MCP provides a single, unified server.

Features

• 🔄 Unified Interface: Aggregates multiple MCP servers into one
• 🧭 CLI Mode for Agents: Preferred workflow for Codex, Claude, and other AI agents using progressive disclosure
• 🔒 OAuth 2.1 Authentication: Production-ready security with scope-based authorization
• ⚡ High Performance: Efficient request forwarding with proper error handling
• 🛡️ Security First: Stdio transport isolation, input sanitization, and comprehensive audit logging
• 🔧 Easy Configuration: Single JSON configuration file with hot-reload support
• 📈 Health Monitoring: Built-in health check endpoints for monitoring and observability

Quick Start

1. Install 1MCP

Binary (Recommended - No Node.js Required):

# Linux/macOS
curl -L https://github.com/1mcp-app/agent/releases/latest/download/1mcp-linux-x64.tar.gz | tar -xz
sudo mv 1mcp /usr/local/bin/

# Windows (PowerShell)
Invoke-WebRequest -Uri "https://github.com/1mcp-app/agent/releases/latest/download/1mcp-win32-x64.zip" -OutFile "1mcp.zip"
Expand-Archive -Path "1mcp.zip" -DestinationPath "."

NPM:

npx -y @1mcp/agent --help

2. Add MCP Servers

1mcp mcp add context7 -- npx -y @upstash/context7-mcp
1mcp mcp add filesystem -- npx -y @modelcontextprotocol/server-filesystem ~/Documents

3. Start the Server

1mcp

4. Choose One Interface

Do not configure the same agent to use both direct MCP access and CLI mode at the same time.

If you switch an agent to CLI mode, remove that agent's existing MCP server configuration first. We recommend CLI mode for Codex, Claude, and similar agent-style sessions.

If you intentionally want direct MCP mode instead, connect that client to the unified endpoint:

For Cursor, add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "1mcp": {
      "url": "http://127.0.0.1:3050/mcp?app=cursor"
    }
  }
}

For VSCode, add to settings.json:

{
  "servers": {
    "1mcp": {
      "url": "http://127.0.0.1:3050/mcp?app=vscode"
    }
  }
}

Install MCP Server to VSCode

For Claude Code:

claude mcp add -t http 1mcp "http://127.0.0.1:3050/mcp?app=claude-code"

Recommended: for Codex, Claude, and other agent-style CLI sessions, the user should run 1mcp cli-setup instead of configuring direct MCP attachment:

1mcp cli-setup --codex
# or
1mcp cli-setup --claude --scope repo --repo-root .

That's it! Your MCP servers stay behind one unified 1MCP runtime, and each agent should use one mode only. For agent sessions, the recommended choice is CLI mode. 🎉

Commands

Core Commands

• 1mcp [serve] - Start the 1MCP server (default command)
• 1mcp mcp add <name> - Add a new MCP server to configuration
• 1mcp mcp list - List all configured MCP servers
• 1mcp mcp status [name] - Show server status and details
• 1mcp instructions - Show the CLI playbook and current server inventory for agent workflows
• 1mcp inspect [target] - Inspect available servers, tools, and tool schemas from a running 1MCP instance
• 1mcp run <server>/<tool> - Call a tool against a running 1MCP instance
• 1mcp cli-setup --codex|--claude - Install bootstrap hooks and startup references for Codex or Claude

For detailed command usage, run: 1mcp <command> --help

CLI Workflow

The user-facing setup step is 1mcp cli-setup. After that, the following CLI workflow is what the AI agent will normally run against a running 1mcp serve instance.

You can still run these commands manually to test the setup:

# Shell 1: start the aggregated MCP server
1mcp serve

# Shell 2: commands the AI agent will normally run
1mcp instructions
1mcp inspect context7
1mcp inspect context7/get-library-docs
1mcp run context7/get-library-docs --args '{"context7CompatibleLibraryID":"/mongodb/docs","topic":"aggregation pipeline"}'

Why CLI Mode?

CLI mode is the preferred path for agent sessions because it replaces broad direct-MCP exposure inside the agent loop with progressive disclosure:

• instructions gives the playbook and current inventory
• inspect <server> narrows discovery to one server
• inspect <server>/<tool> narrows again to one tool schema
• run executes only the chosen tool

This keeps MCP as the backend protocol while giving the agent a thinner frontend workflow with less tool and schema noise in context.

For a given agent, choose only one mode:

• Direct MCP mode: keep that agent's MCP server config
• CLI mode: remove that agent's MCP server config, run cli-setup, and let the AI agent use the CLI workflow

What the user normally runs:

1mcp cli-setup --codex
# or
1mcp cli-setup --claude --scope repo --repo-root .

What the AI agent normally runs after that:

1mcp instructions
1mcp inspect filesystem
1mcp inspect filesystem/read_file
1mcp run filesystem/read_file --args '{"path":"./mcp.json"}'

You may run those commands manually to verify the setup, but they are primarily designed for the agent workflow.

Documentation

📚 Complete Documentation - Comprehensive guides, API reference, and examples

Key Topics

• Installation Guide - Binary, NPM, and Docker installation
• Quick Start - Get running in 5 minutes
• Configuration - CLI flags and environment variables
• Authentication - OAuth 2.1 security setup
• Architecture - How 1MCP works internally
• Development - Contributing and building from source

How It Works

1MCP acts as a proxy, managing and aggregating multiple MCP servers. It starts and stops these servers as subprocesses and forwards requests from AI assistants to the appropriate server. This architecture allows for a single point of entry for all MCP traffic, simplifying management and reducing overhead.

For agent sessions, 1MCP also provides a CLI mode on top of that runtime. MCP remains the interoperability layer behind 1mcp serve; CLI mode changes how the agent discovers and calls tools so the workflow stays selective and scriptable.

Contributing

Contributions are welcome! Please read our CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.