724 tokens across 2 tools — lean (< 1K). Measured 2026-08-16 under methodology v1.0.
| server (self-reported) | ddg-search v1.29.0 |
| status | measured |
| tokenizer | tiktoken / o200k_base |
| launch command | uvx duckduckgo-mcp-server |
| 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 | 61f82494284ccb413e9cb213ed93d7b79f43769229713b2748056a7f1018db14 |
| category | community |
| source | https://github.com/nickclyde/duckduckgo-mcp-server |
| tool | tokens | share | description | schema |
|---|---|---|---|---|
| fetch_content | 387 | 53.5% | 246 | 88 |
| search | 335 | 46.3% | 226 | 58 |
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/duckduckgo/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.