Focused input cuts LLM output tokens by 63% bench on CC with FastAPI

Show HN: Focused input cuts LLM output tokens by 63% bench on CC with FastAPI

Focused input cuts LLM output tokens by 63% bench on CC with FastAPI is a company in show hn launch.

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I built an MCP server (vexp) that pre-indexes a codebase into a dependency graph and serves only relevant code to AI coding agents. While benchmarking it, I found something I wasn't looking for. The expected results were straightforward: less input context → lower cost, fewer tool calls → faster. But the output token reduction was the surprise.Benchmark: 7 tasks on FastAPI (the OSS repo, ~800 Python files), 3 runs/task/arm, 42 total runs, Claude Sonnet 4.6, both arms in --strict-mcp-config isolation. Without graph: ~23 tool calls, ~40K input tokens, 504 output tokens, $0.78/task With graph: ~2.3 tool calls, ~8K input tokens, 189 output tokens, $0.33/task The 58% cost reduction and 22% speed

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Website: vexp.dev