Building an MCP server is one thing; getting an AI agent to actually use your tools is another. A common frustration is that LLMs like Claude Code often ignore well-crafted tools and hallucinate answers instead. This post reveals a surprisingly simple fix: rewriting tool descriptions with precision. By changing just 20 characters, the author saw tool call accuracy jump to 85%. The key is to make descriptions action-oriented, specific, and aligned with how the LLM interprets intent. This is not about tricking the model but about reducing ambiguity. For developers in the AI tooling space, this is a low-effort, high-impact optimization that can be applied across MCP servers, function calling, and agent frameworks. The post also touches on common pitfalls like overly verbose descriptions that dilute the signal. This pattern is evergreen and will remain relevant as LLM-based tool use becomes more widespread.
Learn how refining MCP tool descriptions by just 20 characters can significantly improve LLM tool invocation accuracy, with practical tips for AI tool builders.