238 tokens across 1 tools — lean (< 1K). Measured 2026-08-18 under methodology v1.0.
| server (self-reported) | mcp-fetch v1.29.0 |
| status | measured |
| tokenizer | tiktoken / o200k_base |
| launch command | uvx --with "mcp\<2" mcp-server-fetch |
| 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 | 5d0a1b56f6c08786d7d764b6f4376fba201015e5a5995b6b39c14350bd4aeeb3 |
| category | official-reference |
| source | https://github.com/modelcontextprotocol/servers |
| tool | tokens | share | description | schema |
|---|---|---|---|---|
| fetch | 236 | 99.2% | 60 | 165 |
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/fetch/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.