A study of 4,951 public MCP servers revealed that while around thirty tools per server is optimal, exceeding this number significantly increases the likelihood of models misinterpreting tool functions. This issue arises not just from arithmetic but also from authors using templates to generate tool descriptions, leading to highly similar language across tools. Even well-written descriptions can fail to differentiate a tool's specific purpose if they share too many common words with other tools on the same server, hindering a model's ability to select the correct tool. AI
IMPACT Highlights the importance of clear, distinct tool descriptions for effective AI model operation in complex systems.
RANK_REASON Analysis of a large dataset of MCP servers to identify optimal tool counts and potential issues. [lever_c_demoted from research: ic=1 ai=0.7]
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