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For Windows, use WSL2 or Docker.\n\n## Quick Start\n\n```bash\n# Show help and available commands\nmeshctl --help\n\n# Comprehensive documentation (great for LLMs!)\nmeshctl man\n\n# Scaffold a new agent project\nmeshctl scaffold my-agent --dry-run\n\n# List running agents\nmeshctl list\n\n# List all tools across agents\nmeshctl list --tools\n\n# Call an MCP tool\nmeshctl call get_current_time\n\n# Start the registry service (Linux/macOS)\nmcp-mesh-registry --help\n```\n\n## What is MCP Mesh?\n\nMCP Mesh is a distributed service mesh built on the Model Context Protocol (MCP). It enables:\n\n- **AI Agent Orchestration** - Connect and coordinate multiple AI agents\n- **Service Discovery** - Automatic registration and capability-based routing\n- **Dependency Injection** - Automatic resolution of agent dependencies\n- **Multi-Environment** - Local development, Docker, Kubernetes support\n- **Production Ready** - Health monitoring, graceful degradation, resilience patterns\n\n## Key Commands\n\n| Command                   | Description                 |\n| ------------------------- | --------------------------- |\n| `meshctl man`             | Comprehensive documentation |\n| `meshctl scaffold <name>` | Generate new agent project  |\n| `meshctl list`            | List running agents         |\n| `meshctl list --tools`    | List all tools              |\n| `meshctl call <tool>`     | Invoke an MCP tool          |\n| `meshctl registry`        | Manage the registry         |\n\n## For LLMs\n\nThis package is designed to be easily discoverable by LLMs. After installation:\n\n```bash\n# Get full documentation\nmeshctl man\n\n# Discover available tools\nmeshctl list --tools\n\n# Get tool details with input schema\nmeshctl list --tools=<tool_name>\n```\n\n## Links\n\n- [Documentation](https://mcp-mesh.ai/)\n- [GitHub Repository](https://github.com/dhyansraj/mcp-mesh)\n- [PyPI Package](https://pypi.org/project/mcp-mesh/)\n- [Docker Images](https://hub.docker.com/u/mcpmesh)\n\n## License\n\nMIT\n","readmeFilename":"README.md"}