mcp-context-cost

redis — context cost

9,246 tokens across 53 tools — moderate (5–15K). Measured 2026-09-04 under methodology v1.0.

An Anthropic request carries 7,489 of those tokens as tool definitions, and Claude counts those at 13,221.

   
server (self-reported) Redis MCP Server v1.29.1
status measured
tokenizer tiktoken / o200k_base
launch command uvx --from git+https://github.com/redis/mcp-redis.git redis-mcp-server --url redis://localhost:6379/0
isolation docker · ghcr.io/astral-sh/uv:python3.12-bookworm-slim · network bridge · architecture not on record · network enabled for package fetch; clean FS, no host cred
env vars supplied none
canonical SHA-256 92a01e079c4436e651586a0fa4117e21204dc481de169763e621253280225570
category vendor-official
source https://github.com/redis/mcp-redis

Where the tokens are

tool tokens share description input schema output schema
hybrid_search 510 5.5% 271 140 51
scan_keys 482 5.2% 341 58 43
create_vector_index_hash 352 3.8% 191 100 31
search_redis_documents 345 3.7% 231 31 57
vector_search_hash 319 3.5% 119 123 51
xreadgroup 317 3.4% 138 120 30
scan_all_keys 267 2.9% 143 46 46
json_set 228 2.5% 94 81 29
hset 220 2.4% 73 94 29
xgroup_create 215 2.3% 80 80 30
set 214 2.3% 69 95 28
set_vector_in_hash 207 2.2% 75 68 40
zadd 207 2.2% 75 79 29
zrange 203 2.2% 78 72 29
read_messages 202 2.2% 77 67 34
rename 201 2.2% 91 47 33
xadd 193 2.1% 70 71 29
lrem 191 2.1% 65 76 29
sadd 184 2.0% 64 68 29
xack 182 2.0% 61 69 29
json_get 151 1.6% 56 44 29
json_del 150 1.6% 55 44 29
lrange 149 1.6% 31 55 45
lpush 146 1.6% 17 87 29
rpush 146 1.6% 17 87 29
xgroup_destroy 145 1.6% 46 46 30
xdel 144 1.6% 48 45 29
xrange 143 1.5% 48 44 29
expire 139 1.5% 46 44 28
zrem 139 1.5% 46 42 29

23 smaller tools omitted (2,553 tokens combined) — all of them are in the raw capture.

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.

What this costs on Claude

Measured 2026-09-14 against claude-opus-5 via Anthropic’s count_tokens (method tools-delta/v1).

  tokens  
o200k, full capture 9,246 the badge number — every byte tools/list returned
o200k, Anthropic fields only 7,489 19.0% of the capture is MCP-only metadata
Claude, same fields 13,221 1.43× the badge number

An Anthropic tool definition carries name, description, and input_schema and nothing else, so title, annotations, outputSchema, execution, and icons are dropped before the request — that is the second row. The third row is the same tools counted by Anthropic, which is larger than the second because Anthropic’s tokenizer is denser on this content than o200k_base and the API adds its own framing (at most 328 tokens of it fixed, measured against a single minimal tool). The two effects run in opposite directions, which is why the Claude number is not a fixed multiple of the badge.

Over time

date tokens tools release measured in change
2026-08-17 9,246 53 not recorded not recorded —
2026-08-19 9,246 53 not recorded docker no change
2026-09-02 9,246 53 not recorded docker no change
2026-09-04 9,246 53 1.29.1 docker no change

Some of these sweeps predate the isolation column, so the conditions they were measured under are not on record.

Full series: results/history.csv.

Re-derive it

npx -y mcp-context-cost verify results/redis/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.

Badge JSON · All servers · Leaderboard · Methodology