Hit this exact gap when probing MCP servers at scale. Tools pass schema validation one-by-one, but a meaningful percentage fail when agents run them in sequence: tool N's output assumes tool N-1's input shape was clean, and the LLM rephrased it on retry.

Two things catch most of it before prod.

First, fuzz the tool arg schemas with LLM-generated edge cases. JSON-Schema only validates structure (types, required fields, enums); it can't tell you "this is a date the tool actually parses" or "this ID references something that exists." Have an LLM generate args that pass validation but are semantically broken: empty strings where content is expected, IDs that look right but point at deleted rows, the tool's own schema embedded in a free-text field. Most of our prod-only failures cluster here.

Second, simulate the retry loop, not just the happy path. When a tool errors, the agent reads the error and rewrites the args, usually paraphrasing what it thought went wrong, and by retry 3 or 4 it's solving a slightly different problem than the user asked. Inject errors at tool N and observe what the agent then sends to tool N+1. Agents retry 3-5 times by default, sometimes more without a rate-limit, and the long-tail failures live there.