978 tokens across 5 tools — lean (< 1K). Measured 2026-08-16 under methodology v1.0.
| server (self-reported) | Bright Data v2.11.1 |
| status | measured |
| tokenizer | tiktoken / o200k_base |
| launch command | npx -y @brightdata/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 | API_TOKEN |
| canonical SHA-256 | bd26660364200a7866906bbfecc185e41ea6a42e43ed5d6380b1c3b77a699eae |
| category | vendor-official |
| source | https://github.com/brightdata/brightdata-mcp |
| tool | tokens | share | description | schema |
|---|---|---|---|---|
| discover | 347 | 35.5% | 38 | 281 |
| search_engine_batch | 191 | 19.5% | 17 | 142 |
| search_engine | 186 | 19.0% | 41 | 115 |
| scrape_batch | 141 | 14.4% | 36 | 73 |
| scrape_as_markdown | 115 | 11.8% | 37 | 43 |
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/bright-data/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.