188 tokens across 2 tools — lean (< 1K). Measured 2026-08-16 under methodology v1.0.
| server (self-reported) | mcp-server-qdrant v1.29.0 |
| status | measured |
| tokenizer | tiktoken / o200k_base |
| launch command | uvx mcp-server-qdrant |
| 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 | QDRANT_URL, COLLECTION_NAME |
| canonical SHA-256 | 2bd5a36e7293bab6b8e62c28b7d210c5261f4b80331b176c1df6dc100a7e6fc4 |
| category | vendor-official |
| source | https://github.com/qdrant/mcp-server-qdrant |
| tool | tokens | share | description | schema |
|---|---|---|---|---|
| qdrant-store | 101 | 53.7% | 15 | 74 |
| qdrant-find | 85 | 45.2% | 39 | 30 |
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/qdrant/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.