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a9d543b6f4
- Replace dual Flask/FastAPI setup with a single, unified FastMCP server (`server.py`) - Introduce `MCPDAOWrapper` for secure, context-aware DAO access (`dao_wrapper.py`) - Refactor all MCP tools to be modular and domain-organized (`tools/dashboard/`, `tools/chart/`, `tools/dataset/`, `tools/system/`) - Strongly type all tool contracts with Pydantic v2 models, including full field documentation for LLM/OpenAPI compatibility - Refactor and extend `BaseDAO` for robust, generic CRUD/list operations - Add and update documentation: - Architecture and flow diagrams (`README_ARCHITECTURE.md`) - Tool schema reference and usage instructions (`README.md`, `README_SCHEMAS.md`) - Phase 1 status and roadmap (`README_PHASE1_STATUS.md`) - Implement and test all core list/info tools for dashboards, datasets, and charts, with full search and filter support - Add chart creation tool (`create_chart_simple`) - Provide extension points for Preset-specific auth, RBAC, and logging (stubbed in Phase 1) - Prepare for LLM/agent workflows and future command-based mutations (create/update/delete) - Expand and update unit/integration test coverage for all tools
58 lines
3.1 KiB
Python
58 lines
3.1 KiB
Python
import logging
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import sys
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import traceback
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import json
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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async def main():
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from fastmcp import Client
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logger.info("Starting get_dashboard_info integration test for dashboard IDs 1 through 10")
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issues = [] # Collect (dashboard_id, message) for any warnings/errors
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async with Client("http://localhost:5008/mcp") as client:
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for dashboard_id in range(1, 11):
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logger.info(f"\n---\nCalling get_dashboard_info with dashboard_id={dashboard_id}")
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try:
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logger.info(f"Sending request: {{'dashboard_id': {dashboard_id}}}")
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result = await client.call_tool("get_dashboard_info", {"dashboard_id": dashboard_id})
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logger.info(f"Raw result object: {result}")
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logger.info(f"Result type: {type(result.data)}")
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logger.info(f"get_dashboard_info output for id={dashboard_id} (repr): {repr(result.data)}")
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# Pretty-print output
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if hasattr(result.data, "model_dump"):
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as_dict = result.data.model_dump()
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logger.info(f"get_dashboard_info output for id={dashboard_id} (dict): {as_dict}")
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pretty = json.dumps(as_dict, indent=2, default=str)
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logger.info(f"get_dashboard_info output for id={dashboard_id} (pretty):\n{pretty}")
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# Detect error/warning fields
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if as_dict.get('error') or as_dict.get('error_type'):
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issues.append((dashboard_id, f"Error: {as_dict.get('error')} | Type: {as_dict.get('error_type')}"))
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elif isinstance(result.data, dict):
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logger.info(f"get_dashboard_info output for id={dashboard_id} (dict): {result.data}")
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pretty = json.dumps(result.data, indent=2, default=str)
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logger.info(f"get_dashboard_info output for id={dashboard_id} (pretty):\n{pretty}")
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if result.data.get('error') or result.data.get('error_type'):
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issues.append((dashboard_id, f"Error: {result.data.get('error')} | Type: {result.data.get('error_type')}"))
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else:
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msg = f"Output for id={dashboard_id} is not a dict or Pydantic model. Type: {type(result.data)}. Value: {result.data}"
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logger.warning(msg)
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issues.append((dashboard_id, msg))
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except Exception as e:
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msg = f"Exception calling get_dashboard_info with id={dashboard_id}: {e}"
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logger.error(msg)
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logger.error(traceback.format_exc())
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issues.append((dashboard_id, msg))
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# Summary
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logger.info("\n=== SUMMARY ===")
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if issues:
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logger.warning(f"Found issues with the following dashboards:")
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for dashboard_id, msg in issues:
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logger.warning(f" Dashboard {dashboard_id}: {msg}")
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else:
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logger.info("All dashboards 1-10 returned successfully with no errors or warnings.")
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if __name__ == "__main__":
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import asyncio
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asyncio.run(main())
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