"""
Connect: erzeugt saubere, getestete Konfig-Snippets für IDEs/Agenten auf dem
LOKALEN PC (separate Maschine im LAN). Alle zeigen auf den **Gateway** der Box
(`model: auto`, LiteLLM :4000) + den **Shared-Memory-MCP** (MC :9000).

Wichtig: Host ist die LAN-IP der Box (NICHT eine Proxy-Domain) — das war in v1
die häufigste Fehlerquelle. Der Aufrufer übergibt den Host explizit.
"""

import json

DEFAULT_HOST = "192.168.178.151"
GATEWAY_PORT = 4000
MC_PORT = 9000

# Modelle, die der Gateway anbietet (model:auto = Standard).
GATEWAY_MODELS = ["auto", "fast", "heavy", "coder", "vision"]


def _gw(host: str) -> str:
    return f"http://{host}:{GATEWAY_PORT}/v1"


def build_snippets(host: str = DEFAULT_HOST,
                   mcp_script_path: str = r"C:\\Users\\TobisPC\\mission-control-v2\\mcp\\mcp_memory.py",
                   mcp_python: str = "python") -> dict:
    gw = _gw(host)
    mc_url = f"http://{host}:{MC_PORT}"

    cline = json.dumps({
        "apiProvider": "openai",
        "openAiBaseUrl": gw,
        "openAiApiKey": "local",
        "openAiModelId": "auto",
    }, indent=2)

    opencode = json.dumps({
        "providers": {
            "litellm": {
                "npm": "@ai-sdk/openai-compatible",
                "name": "Bosgame Gateway",
                "options": {"baseURL": gw, "apiKey": "local"},
                "models": {m: {"name": m} for m in GATEWAY_MODELS},
            }
        }
    }, indent=2)

    zed = json.dumps({
        "language_models": {
            "openai_compatible": {
                "bosgame": {
                    "api_url": gw,
                    "available_models": [
                        {"name": m, "display_name": m, "max_tokens": 131072,
                         "capabilities": {"tools": True}} for m in GATEWAY_MODELS
                    ],
                }
            }
        }
    }, indent=2)

    cont = json.dumps({
        "models": [
            {"title": f"Bosgame / {m}", "provider": "openai", "model": m,
             "apiBase": gw, "apiKey": "local"} for m in ("auto", "coder", "heavy")
        ]
    }, indent=2)

    # Claude Code: Anthropic-Format → LiteLLM kann /v1/messages anbieten.
    claude_code = (
        f'# Claude Code gegen den lokalen Gateway (Anthropic-kompatibel via LiteLLM):\n'
        f'export ANTHROPIC_BASE_URL="http://{host}:{GATEWAY_PORT}"\n'
        f'export ANTHROPIC_API_KEY="local"\n'
        f'export ANTHROPIC_MODEL="auto"\n'
        f'# (LiteLLM muss den Anthropic-/v1/messages-Endpunkt aktiviert haben — auf der Box prüfen.)'
    )

    memory_mcp = json.dumps({
        "mcpServers": {
            "mission-control-memory": {
                "command": mcp_python,
                "args": [mcp_script_path],
                "env": {"MC_URL": mc_url},
            }
        }
    }, indent=2)

    return {
        "host": host,
        "gateway_url": gw,
        "tools": {
            "cline": {"label": "Cline (VS Code)", "lang": "json", "snippet": cline,
                      "note": "OpenAI-Provider → Gateway. Modell 'auto' (schnell, eskaliert bei Bedarf)."},
            "opencode": {"label": "OpenCode", "lang": "jsonc", "snippet": opencode,
                         "note": "Datei opencode.jsonc, Key 'providers'."},
            "zed": {"label": "Zed", "lang": "json", "snippet": zed,
                    "note": "settings.json → language_models.openai_compatible."},
            "continue": {"label": "Continue", "lang": "json", "snippet": cont,
                         "note": "~/.continue/config.json."},
            "claude_code": {"label": "Claude Code", "lang": "bash", "snippet": claude_code,
                            "note": "Anthropic-Format über LiteLLM /v1/messages."},
            "memory_mcp": {"label": "Shared Memory (MCP)", "lang": "json", "snippet": memory_mcp,
                           "note": "Für jedes MCP-fähige Tool. mcp_memory.py muss lokal liegen."},
        },
    }