319 tokens across 2 tools — lean (< 1K). Measured 2026-08-16 under methodology v1.0.
| server (self-reported) | example-servers/brave-search v0.1.0 |
| status | measured |
| tokenizer | tiktoken / o200k_base |
| launch command | npx -y @modelcontextprotocol/server-brave-search |
| 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 | BRAVE_API_KEY |
| canonical SHA-256 | 727577fbcd5fe4e811453a32d9c9852ea877c90b8acf56e0cdf5eda15e97d803 |
| category | official-reference |
| source | https://github.com/modelcontextprotocol/servers-archived |
| tool | tokens | share | description | schema |
|---|---|---|---|---|
| brave_web_search | 161 | 50.5% | 65 | 84 |
| brave_local_search | 156 | 48.9% | 82 | 61 |
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/brave-search-legacy/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.