133 tokens across 1 tools — lean (< 1K). Measured 2026-08-16 under methodology v1.0.
| server (self-reported) | example-servers/perplexity-ask v0.1.0 |
| status | measured |
| tokenizer | tiktoken / o200k_base |
| launch command | npx -y server-perplexity-ask |
| 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 | PERPLEXITY_API_KEY |
| canonical SHA-256 | bf3f548ecca0308f30e544a03d3a36778dbbfba3a5ed12dab6d981dcbcc7a73c |
| category | vendor-official |
| source | https://github.com/perplexityai/modelcontextprotocol |
| tool | tokens | share | description | schema |
|---|---|---|---|---|
| perplexity_ask | 131 | 98.5% | 38 | 80 |
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/perplexity/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.