mcp-context-cost

memory — context cost

2,378 tokens across 9 tools — light (1–5K). Measured 2026-09-28 under methodology v1.0.

An Anthropic request carries 901 of those tokens as tool definitions, and Claude counts those at 1,880.

   
server (self-reported) memory-server v0.6.3
status measured
tokenizer tiktoken / o200k_base
launch command npx -y @modelcontextprotocol/server-memory@2026.7.4
isolation docker · public.ecr.aws/docker/library/node:22-slim · network bridge · linux/amd64 · network enabled for package fetch; clean FS, no host credentials
env vars supplied none
canonical SHA-256 d028274f76dc9aa2e622ae02a17ce313aa65d7b5935254ca53889d4094238abb
category official-reference
source https://github.com/modelcontextprotocol/servers

Where the tokens are

tool tokens share description input schema output schema
search_nodes 323 13.6% 11 51 207
open_nodes 322 13.5% 10 51 207
create_entities 294 12.4% 8 111 121
create_relations 294 12.4% 17 106 116
read_graph 291 12.2% 5 24 207
add_observations 249 10.5% 10 97 85
delete_relations 225 9.5% 7 115 48
delete_observations 212 8.9% 9 98 48
delete_entities 166 7.0% 11 53 48

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 2,378 the badge number — every byte tools/list returned
o200k, Anthropic fields only 901 62.1% of the capture is MCP-only metadata
Claude, same fields 1,880 0.79× 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
2026-08-16 2,378 9 not recorded not recorded —
2026-08-17 2,378 9 not recorded not recorded no change
2026-08-18 2,378 9 not recorded docker no change
2026-08-19 2,378 9 not recorded docker no change
2026-08-24 2,378 9 not recorded docker no change
2026-08-31 2,378 9 not recorded docker no change
2026-09-03 2,378 9 not recorded docker no change
2026-09-04 2,378 9 0.6.3 docker no change
2026-09-05 2,378 9 0.6.3 docker no change
2026-09-07 2,378 9 0.6.3 docker no change
2026-09-14 2,378 9 0.6.3 docker no change
2026-09-21 2,378 9 0.6.3 docker no change
2026-09-28 2,378 9 0.6.3 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/memory/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