Unverified50% confidenceCautionExact time
If Tool Search's retrieval recall is inaccurate, the model may not get the correct tool, and the semantic clarity of tool names and namespace descriptions directly affects the final result.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIf the data always provides tools that get used, the model learns the erroneous association that it must use a tool whenever it sees one.74% similarUnverifiedWhen multiple tools are semantically similar, the model's tool selection is essentially a multi-class classification problem, and the more candidate classes there are, the higher the misclassification rate.74% similarUnverifiedWhen tool descriptions fill the context window, models are more prone to selecting the wrong tool among many similar ones.71% similarUnverifiedWhen a model fails to correctly invoke retrieval tools or determines that a question does not require a search, it falls back to answering from training data, thereby exposing the knowledge cutoff issue.70% similarUnverifiedWhen the number of tools increases and tools with similar names and overlapping functions appear (such as get_x, fetch_x, read_x), the LLM's tool selection becomes unreliable.69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1007863API
curl https://kongchang.com/api/v1/knowledge/claims/1007863MCP
get_claim(id=1007863)