ToolSDK.ai ensures tool synergy efficiency through the following architectural design:
- Intelligent Routing: Automatically selects the optimal MCP server based on the type of task (e.g., text processing prioritizes calls to GPT-4)
- parallel processing: Support for scheduling multiple tools at the same time for a single prompt (
tools: [github,slack]) - Results Cache: Automatically caching API responses to high-frequency queries
- link monitoring: Provide time statistics and error logs for each tool call.
Actual tests show that in a scenario where 10 tools collaborate, the overall latency is reduced from 15 seconds in the traditional solution to less than 3 seconds. Developers can use theToolSDK.monitor()method to view performance metrics in real time.
This answer comes from the articleToolSDK.ai: a free SDK to quickly connect AI tools to MCP serversThe
May not be reproduced without permission:AI productivity tools " How to improve performance issues with multiple AI tools working together?
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