Claude Code has become my primary interface for iterating on data pipeline work — specifically, normalizing government regulatory filings (XBRL across three different accounting standards) and exposing them via REST and MCP.

The MCP piece is where the workflow gets interesting. Instead of building a client that calls endpoints, you describe tools declaratively and the model decides when to invoke them. For financial data this is surprisingly effective — a query like "compare this company's leverage trend to sector peers over 10 years" gets decomposed automatically into the right sequence of tool calls without you hardcoding that logic.

One thing I haven't seen discussed much: tool latency sensitivity is much higher in conversational MCP use than in batch pipelines. A 2s tool response feels fine in a script but breaks conversational flow. We ended up caching frequently accessed tables in-memory (~26MB) to get sub-100ms responses. Have you noticed similar thresholds where latency starts affecting the quality of the model's reasoning chain?