ToolMinimize: Auditing and Rewriting LLM Agent Tool Calls to Minimize Privacy Exposure

LLM agents routinely include privacy-sensitive data (PSD) in tool call arguments beyond what the invoked tools require, crossing trust boundaries to third-party services on every invocation. A controlled measurement on three production LLMs (GPT-4o, Claude 3.5 Sonnet, Llama-3.3-70B) shows that 81--88% of tool calls include unnecessary PSD under default prompts; explicit privacy instructions still leave 36--76% over-sharing. Existing defenses gate calls (allow/block) or label flows (information-flow control) but cannot rewrite argument values, and PII detection tools miss implicit PSD like ``Memorial Sloan Kettering'' (a hospital name that implies a diagnosis). We present, a middleware that intercepts tool calls and rewrites their arguments to the minimum data necessary for tool functionality, combining schema-aware necessity analysis with four operations: removal, generalization, substitution, and truncation. Live validation on 307 tool calls across the three LLMs above reduces privacy cost by 81.2--92.0% at 100% argument-level task validity (TOST equivalence p < 0.001 at Δ = 1.0); on 25 unannotated Model Context Protocol (MCP) schemas, by 79.0% with no minimum_necessary metadata. An optional LLM content-necessity layer strips task-irrelevant PSD from otherwise-necessary free-text fields, raising live-LLM reduction to 85.1--95.6% and author-schema reduction from 71.1% to 90.9%. Median latency is 1.77,ms.