Context window limits, not APIs, are the real bottleneck for MCP servers

A developer building an HTTPS-based MCP server to connect AskElephant's call-recording tool with claude.ai discovered that the core design challenge was not API integration but context window arithmetic. An analysis of 3,706 client call engagements showed that a single transcript could reach nearly 44,000 tokens, and a typical multi-meeting search query could naively generate over 325,000 tokens — far exceeding a 200,000-token context window. To solve this, the team split responsibilities so that a Worker function scans and filters transcripts locally, returning only relevant excerpts with speaker labels and timestamps, while Claude handles reasoning over the condensed output. This approach reduced a real 40-meeting search from a theoretical 325,000 tokens to roughly 19,200 tokens of targeted excerpts. The team also identified a compounding bug where three independently reasonable caps on meetings, hits, and context characters were never validated as a combined budget, allowing worst-case outputs to silently exceed the window limit.