MCP
Arxo MCP
Section titled “Arxo MCP”The Arxo MCP (Model Context Protocol) server exposes agent architecture analysis to AI assistants such as Cursor and Claude. Use it to run analyses on agent and LLM-powered code, get reliability and governance metrics, and manage your arxo.yaml configuration from your IDE or chat.
Supported Languages
Section titled “Supported Languages”The MCP server analyzes TypeScript and Python for agent architecture. Create an arxo.yaml in your project root (optional) to configure analysis; see Configuration for config options.
When to Use MCP vs CLI vs IDE
Section titled “When to Use MCP vs CLI vs IDE”| Use case | Best option |
|---|---|
| Chat-driven analysis (“Run agent architecture analysis on this project”) | MCP — AI calls tools on demand |
| Pre-commit hooks, CI/CD pipelines | CLI — arxo analyze |
| Real-time analysis as you code, inline violations | IDE extension — Arxo VS Code extension |
MCP is ideal when you want to ask natural-language questions and get agent architecture insights within the AI chat, without leaving your editor.
Features
Section titled “Features”- Agent and OpenClaw architecture analysis — Run analysis to get reliability, governance, safety, and coordination metrics for agent/LLM code and OpenClaw config/skill/observability metrics
- Config help — Get schema, validate config, or generate a suggested
arxo.yamlfor your project
How it works
Section titled “How it works”You install the arxo-mcp binary and configure your MCP client (e.g. Cursor) to run it. The server communicates over stdio using the Model Context Protocol. AI assistants can then call analyze_architecture to run analysis or use the config tools to work with arxo.yaml.
Next Steps
Section titled “Next Steps”- Quick Start — Get running in under 5 minutes
- Installation — Get the binary and verify
- Configuration — Cursor and other MCP client setup
- Tools — Available MCP tools
- Workflows — Example AI assistant workflows