Prompt-level glossary injection can recover ASR-corrupted domain terms, but its effectiveness depends sharply on glossary scope. In enterprise meeting summarization, a black-box STT system passes only a 1-best transcript to the summarizer; its errors on loanwords, acronyms, and domain terms can replace the canonical forms needed downstream. Motivated by the keyword-input UX of a deployed Korean meeting summarization service, we evaluate training-free prompt-level injection of an annotation-derived meeting-scoped glossary proxy without transcript rewriting. On 30 public Korean meetings processed offline by operational STT, 27% of annotated terms are corrupted under normalized matching, and a no-glossary summarizer recovers only 1.3% of these corrupted terms. Meeting-scoped injection raises term recovery to 53.8%, with 89.2% of output keywords grounded in the transcript. The effect collapses under broader scopes: on the same 45 terms, meeting-level recovery is 66.7% versus 6.7% for a 2,669-term full-dictionary proxy. Coverage-controlled size sweeps show this collapse on Qwen3-8B, and three EXAONE-3.5 points reproduce it, indicating prompt-level over-biasing. A four-annotator human evaluation does not establish improved summary utility; we therefore treat domain-term recovery as an operational dimension, not a summary-preference proxy. We contribute a public-data evaluation protocol and deployment analysis of domain-term recovery under black-box STT constraints.