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Using with AI Agents

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Machine-readable API reference

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Using with Claude Code

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Via MCP Server

# Install the MCP server
npm install -g @stagingspaces/mcp-server

# Or add to your Claude Code config
claude mcp add stagingspaces -- npx @stagingspaces/mcp-server

“Stage this room photo with a scandinavian style”

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Via llms.txt

“Read https://stagingspaces-production-6fb7.up.railway.app/llms.txt and use the StagingSpaces API to stage my room photos”

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Using with GPT / Custom GPTs

1. Create a Custom GPT
2. Add the OpenAPI spec as an Action: /docs/openapi.yaml
3. Configure authentication with your API key
4. The GPT can now stage images on your behalf

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Using with any agent framework

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LangChain

from langchain.tools import tool
import requests

@tool
def stage_room(image_path: str, style: str = "modern") -> dict:
    """Stage an empty room photo with AI-placed furniture.

    Args:
        image_path: Path to the room image file
        style: Design style (modern, scandinavian, farmhouse, etc.)
    """
    response = requests.post(
        "https://stagingspaces-production-6fb7.up.railway.app/api/v1/stage",
        headers={"Authorization": f"Bearer {API_KEY}"},
        files={"image": open(image_path, "rb")},
        data={"style": style}
    )
    return response.json()

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CrewAI / AutoGPT

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Agent-friendly design decisions

• Simple auth: Single Bearer token, no OAuth flows
• Multipart uploads: Standard file upload format all HTTP libraries support
• Predictable responses: Consistent JSON structure across all endpoints
• Claim codes: Human-readable codes (SS-7X9K2M) agents can relay to users
• Idempotent reads: GET endpoints are safe to call repeatedly
• Clear errors: Error messages include actionable fix suggestions