from anthropic import AsyncAnthropic
from dedalus_mcp import types

anthropic = AsyncAnthropic()

async def sampling_handler(
    _ctx: object,
    params: types.CreateMessageRequestParams,
) -> types.CreateMessageResult | types.ErrorData:
    try:
        messages = [{"role": m.role, "content": m.content.text} for m in params.messages]

        resp = await anthropic.messages.create(
            model="claude-sonnet-4-20250514",
            messages=messages,
            max_tokens=params.maxTokens,
        )

        return types.CreateMessageResult(
            model=resp.model,
            role="assistant",
            content=types.TextContent(type="text", text=resp.content[0].text),
            stopReason="end_turn",
        )
    except Exception as e:
        return types.ErrorData(code=types.INTERNAL_ERROR, message=str(e))

capabilities = ClientCapabilitiesConfig(sampling=sampling_handler)

async with open_connection(
    url="http://127.0.0.1:8000/mcp",
    capabilities=capabilities,
) as client:
    # यदि सर्वर इस tool रन के दौरान sampling/createMessage को कॉल करता है,
    # तो आपका sampling_handler invoke किया जाएगा।
    result = await client.call_tool("analyze", {"data": "..."})

from dedalus_mcp import types

async def sampling_handler(context: object, params: types.CreateMessageRequestParams):
    try:
        # ... LLM को कॉल करें ...
        return types.CreateMessageResult(
            model="claude-3-5-sonnet-20241022",
            role="assistant",
            content=types.TextContent(type="text", text="ok"),
            stopReason="end_turn",
        )
    except Exception as e:
        return types.ErrorData(code=types.INTERNAL_ERROR, message=str(e))