992 tokens across 1 tools — lean (< 1K). Measured 2026-08-16 under methodology v1.0.
| server (self-reported) | sequential-thinking-server v0.2.0 |
| status | measured |
| tokenizer | tiktoken / o200k_base |
| launch command | npx -y @modelcontextprotocol/server-sequential-thinking |
| 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 | none |
| canonical SHA-256 | 5dcda6f07c1a8f8850c8800434f0fac0e7fe6c869de09716e5bc15c7da8e5de6 |
| category | official-reference |
| source | https://github.com/modelcontextprotocol/servers |
| tool | tokens | share | description | schema |
|---|---|---|---|---|
| sequentialthinking | 990 | 99.8% | 565 | 259 |
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/sequential-thinking/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.