1,653 tokens across 5 tools — light (1–5K). Measured 2026-08-16 under methodology v1.0.
| server (self-reported) | tavily-mcp v0.2.22 |
| status | measured |
| tokenizer | tiktoken / o200k_base |
| launch command | npx -y tavily-mcp |
| isolation | docker · public.ecr.aws/docker/library/node:22-slim · network bridge · network enabled for package fetch; clean FS, no host credentials |
| env vars supplied | TAVILY_API_KEY |
| canonical SHA-256 | b2bcd45339fe42caa0f55e5ca1196091b2dcda4b868fedd209177b54f2ef51ef |
| category | vendor-official |
| source | https://github.com/tavily-ai/tavily-mcp |
| tool | tokens | share | description | schema |
|---|---|---|---|---|
| tavily_search | 615 | 37.2% | 29 | 574 |
| tavily_crawl | 412 | 24.9% | 20 | 379 |
| tavily_map | 288 | 17.4% | 18 | 258 |
| tavily_extract | 178 | 10.8% | 15 | 151 |
| tavily_research | 158 | 9.6% | 50 | 95 |
Each tool is tokenized on its own, so the parts do not sum exactly to the whole: the array adds its own brackets and commas, and the tokenizer merges tokens across object boundaries. The badge number is always the count of the whole array, never a sum of parts.
npx -y mcp-context-cost verify results/tavily/measurement.json
That re-tokenizes the published capture and checks the count and the hash. If it disagrees with the badge, the badge is wrong — open an issue and it gets corrected.