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- 来源: blogs_podcasts
- 原始来源: https://aws.amazon.com/blogs/machine-learning/mcp-tool-design-practical-approaches-and-tradeoffs
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When Model Context Protocol (MCP) tools underperform, the cause is rarely the protocol itself but the tool design. Many teams start by exposing an existing API as-is and trusting the agent to figure out the rest. It is a natural way to extend APIs to agentic systems and generative AI coding tools. For straightforward use cases, it can work. But often it does not.
You must design your tools for how large language models (LLMs) and agentic systems work. Without this, you risk failed tool calls, wrong parameter values, and retries that waste context and degrade performance. In this post, we show where MCP tool design goes wrong and how to fix it with practical context engineering approaches.
Two problems are behind most of these failures. The first is bloat .…
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