293 tokens across 2 tools — lean (< 1K). Measured 2026-08-18 under methodology v1.0.
| server (self-reported) | mcp-time v1.29.0 |
| status | measured |
| tokenizer | tiktoken / o200k_base |
| launch command | uvx --with "mcp\<2" mcp-server-time |
| isolation | docker · ghcr.io/astral-sh/uv:python3.12-bookworm-slim · network bridge · network enabled for package fetch; clean FS, no host credentials |
| env vars supplied | none |
| canonical SHA-256 | 7c6ff08840620894a66a103e02a8eb315013dd0a7d7c6cfb59b0fbe51f8a5d87 |
| category | official-reference |
| source | https://github.com/modelcontextprotocol/servers |
| tool | tokens | share | description | schema |
|---|---|---|---|---|
| convert_time | 186 | 63.5% | 5 | 143 |
| get_current_time | 105 | 35.8% | 7 | 59 |
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/time/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.