Background to the issue
In AI agent or cross-framework development scenarios, developers often need to repeatedly write similar data access tools for different frameworks such as LangChain, LlamaIndex, etc., leading to inefficiency and maintenance difficulties.
prescription
Use the MCP Toolbox'sCentralized management of toolsfunction solves the problem completely:
- Unified platform storage:Define tools in tools.yaml (e.g. search-hotels-by-name), all frameworks share the same set of tool configurations
- SDK Adaptation:The Python example for loading tools via SDKs specific to each framework (e.g. toolbox-langchain) is as follows:
from toolbox_langchain import ToolboxClient
async with ToolboxClient("http://localhost:5000") as client:
tools = await client.load_toolset("hotel_booking") - Dynamic Updates:Modify tools.yaml to automatically synchronize to all applications without restarting the service.
Implementation of recommendations
1. Tools categorized by business area (e.g., order management, user authentication)
2. Managing database credentials using ${ENV_VAR}
3. Ensure environment consistency in conjunction with Docker deployment
This answer comes from the articleMCP Toolbox for Databases: MCP services for fast database operationsThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of duplicated development of database tools in a multi-framework development environment?
