Hermes
8f4883bfcd
DashCaddy is now controllable by ANY AI agent via Model Context Protocol. 17 MCP tools exposed: - Service management: list, get, health check - Container management: list, start/stop/restart/remove - Deployment: deploy app, wizard recommendations, catalog search, discovery - System: health, metrics, diagnostics - Infrastructure: DNS listing, Caddyfile generation - Backup & Recovery: create backup, status - Fleet: list hosts Protocol: JSON-RPC 2.0 over stdio Connection: DASHCADDY_URL + DASHCADDY_API_KEY env vars Any MCP-compatible agent (Claude Desktop, Hermes, GPT) can now: 'I want to stream movies' → wizard recommends Plex/Sonarr/Radarr 'Deploy Plex' → container + Caddyfile + DNS + health check 'Why is Plex down?' → diagnostics with structured findings 'Back up everything' → full snapshot 14 tests, 1752 total pass.
57 lines
2.6 KiB
Markdown
57 lines
2.6 KiB
Markdown
# DashCaddy AI-Native Vision
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## The Vision
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DashCaddy should be inherently optimized for AI agents to control it.
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Users should be able to self-host anything using natural language.
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## Core Principles
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1. **AI as first-class citizen** — not a bolt-on chatbot, but where the API itself is designed for AI consumption
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2. **Natural language → deployment** — "host a Plex server" → running container + reverse proxy + DNS + health check
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3. **Agent-friendly API** — structured responses, semantic error codes, state machines, idempotent operations
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4. **MCP-native** — DashCaddy should expose itself as an MCP server so any AI agent can control it
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## Architecture Layers
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### Layer 1: Natural Language Intent Router (NEW)
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`POST /api/v1/ai/intent` — Takes natural language, returns structured action plan
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- "I want to stream movies" → { category: media-streaming, recommended: [plex, sonarr, radarr] }
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- "Set up a password manager" → { category: file-sync, recommended: [vaultwarden] }
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- "Block ads on my network" → { category: home-network, recommended: [adguard] }
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- "Why is Plex down?" → diagnostics query → { action: health-check, service: plex }
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### Layer 2: MCP Server (NEW)
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Expose DashCaddy as a Model Context Protocol server so ANY AI agent (Claude, GPT, Gemini, Hermes) can:
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- List services, containers, health status
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- Deploy/stop/restart apps
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- Manage DNS records and Caddyfile routes
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- Run diagnostics and get structured results
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- Create backups and restore
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### Layer 3: Structured Action API (EXISTING — needs enhancement)
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366 existing routes already cover the CRUD surface. Enhancement needed:
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- Consistent response envelopes (already have `ok()` / `errorResponse()`)
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- All error responses include machine-readable codes (DC-086 done — 80 codes)
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- Idempotency keys for mutating operations
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- Operation receipts (UUID + status tracking)
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### Layer 4: Semantic Service Catalog (EXISTING — DC-104)
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76 templates with categories, auto-categorization, search.
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Enhancement: Add intent tags ("movie streaming", "password manager", "ad blocking")
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### Layer 5: Diagnostic Engine (NEW)
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`POST /api/v1/ai/diagnose` — Structured troubleshooting
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- "Why is X slow?" → checks: CPU, memory, network, disk I/O, container logs
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- Returns structured findings with severity + suggested fix
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- Can auto-apply fixes with user approval
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### Layer 6: Deployment Orchestrator (PARTIAL — DC-103 + wizard)
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"Deploy Plex" → full automation chain:
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1. Pull image
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2. Create container with optimal config
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3. Generate Caddyfile route (DC-106)
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4. Create DNS record
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5. Add to services list
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6. Start health monitoring
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7. Configure notifications
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8. Return ready-to-use URL
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