1,425 tokens across 1 tools — light (1–5K). Measured 2026-08-16 under methodology v1.0.
| server (self-reported) | mcp-pandoc v0.11.1 |
| status | measured |
| tokenizer | tiktoken / o200k_base |
| launch command | uvx mcp-pandoc |
| 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 | 4d8f9de25d7aa74f0fb5e0c828a904d74b3934e62cb1032bc6cf0fada1f0d952 |
| category | community |
| source | https://github.com/vivekVells/mcp-pandoc |
| tool | tokens | share | description | schema |
|---|---|---|---|---|
| convert-contents | 1,423 | 99.9% | 996 | 361 |
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/pandoc/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.