// MCP TOKEN COST

The Awslabs Valkey MCP server costs 9,719 tokens before the first call.

Connect Awslabs Valkey and its 103 tool definitions are loaded into the model's context on every request — 4.9% of a 200k window spent before your agent does anything.

QUICK ANSWER The Awslabs Valkey MCP server's tool definitions consume 9,719 tokens — 5.1× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 103 tools · 9,719 tokens · 4.9% of 200k · 1.0% of 1M Method →

// CONTEXT WINDOW SHARE

What that buys before your agent starts working.

Tool definitions are overhead: they occupy context on every request and compete with your code, documents and conversation history for the same window.

200K WINDOW 4.9%

1M WINDOW 1.0%

Corpus context: Awslabs Valkey ranks #177 of 3,213 measured MCP servers by definition cost. The median is 1,905 tokens, p90 is 7,952, and the heaviest (Fusionauth) is 183,337 — 92% of a 200k window on its own.

// PER-TOOL BREAKDOWN

Where the 9,719 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 94 tokens per tool.

Tool	Category	Tokens	% of server
sorted_set_range_by_score	Write	186	1.9%
string_set	Write	173	1.8%
sorted_set_range_by_lex	Write	170	1.7%
stream_read_group	Read	160	1.6%
json_set	Write	149	1.5%
stream_range	Execute	148	1.5%
stream_add	Write	147	1.5%
bitmap_pos	Execute	146	1.5%
sorted_set_range	Write	145	1.5%
json_get	Read	143	1.5%
rename	Write	143	1.5%
list_move	Read	139	1.4%
list_position	Read	137	1.4%
json_arrindex	Execute	131	1.3%
stream_group_create	Write	130	1.3%
stream_read	Read	124	1.3%
json_arrtrim	Execute	122	1.3%
sorted_set_remove_by_lex	Write	114	1.2%
list_remove	Read	111	1.1%
json_arrpop	Read	108	1.1%
list_range	Read	108	1.1%
sorted_set_add_incr	Write	107	1.1%
bitmap_count	Read	106	1.1%
json_numincrby	Read	105	1.1%
json_nummultby	Read	105	1.1%
bitmap_set	Write	105	1.1%
sorted_set_rank	Write	105	1.1%
sorted_set_remove_by_score	Write	105	1.1%
stream_group_delete_consumer	Destructive	103	1.1%
stream_trim	Read	102	1.0%
stream_group_set_id	Write	102	1.0%
hash_increment	Read	101	1.0%
json_strappend	Read	101	1.0%
sorted_set_cardinality	Write	101	1.0%
list_set	Read	99	1.0%
hash_set_if_not_exists	Write	99	1.0%
sorted_set_remove_by_rank	Write	99	1.0%
list_trim	Read	96	1.0%
json_arrappend	Read	94	1.0%
string_get_range	Read	94	1.0%
string_set_range	Write	94	1.0%
hash_random_field_with_values	Read	90	0.9%
list_insert_after	Read	90	0.9%
list_insert_before	Read	90	0.9%
hash_set	Write	90	0.9%
expire	Write	89	0.9%
set_move	Write	89	0.9%
json_type	Write	87	0.9%
json_objlen	Read	86	0.9%
set_pop	Write	86	0.9%
set_random_member	Write	86	0.9%
sorted_set_popmax	Write	86	0.9%
sorted_set_popmin	Write	86	0.9%
hash_random_field	Read	84	0.9%
json_objkeys	Read	84	0.9%
list_pop_left	Read	84	0.9%
list_pop_right	Read	84	0.9%
string_decrement	Read	84	0.9%
hll_add	Write	84	0.9%
list_get	Read	83	0.9%
stream_info_consumers	Read	83	0.9%
json_clear	Destructive	82	0.8%
bitmap_get	Read	82	0.8%
json_arrlen	Read	82	0.8%
string_increment	Read	82	0.8%
json_strlen	Read	81	0.8%
sorted_set_add	Write	81	0.8%
hash_strlen	Read	80	0.8%
json_toggle	Read	80	0.8%
stream_group_destroy	Destructive	79	0.8%
list_prepend_multiple	Read	79	0.8%
hash_set_multiple	Write	79	0.8%
sorted_set_score	Write	79	0.8%
json_del	Destructive	78	0.8%
string_get_set	Read	78	0.8%
hash_exists	Read	77	0.8%
list_append_multiple	Read	77	0.8%
type	Write	77	0.8%
stream_delete	Destructive	76	0.8%
list_prepend	Read	76	0.8%
string_increment_float	Read	76	0.8%
hash_get	Read	75	0.8%
info	Read	75	0.8%
string_append	Read	75	0.8%
set_add	Write	75	0.8%
set_contains	Write	75	0.8%
set_remove	Write	75	0.8%
list_append	Read	74	0.8%
sorted_set_remove	Write	71	0.7%
hll_count	Read	70	0.7%
hash_get_all	Read	66	0.7%
delete	Destructive	64	0.7%
hash_keys	Read	63	0.6%
set_cardinality	Write	63	0.6%
hash_length	Read	62	0.6%
stream_info_groups	Read	62	0.6%
hash_values	Read	61	0.6%
set_members	Write	61	0.6%
stream_info	Read	59	0.6%
list_length	Read	58	0.6%
stream_length	Read	58	0.6%
string_get	Read	57	0.6%
string_length	Read	57	0.6%

Computed over 103 of 105 catalogued tools — the remainder have no published input schema, so the true total is slightly higher.

// SCOPED GRANT

Most agents use a handful of these tools. They pay for all 103.

A PolicyLayer grant exposes only the tools you allow — ungranted definitions are filtered out of the tool list, so they never enter the context window. Estimates below assume typical-weight tools (94 tokens each).

Grant scope	Definition cost	Reduction
All 103 tools (no gateway)	9,719 tokens	—
3 granted tools	~283 tokens	−97%
5 granted tools	~472 tokens	−95%
10 granted tools	~944 tokens	−90%

Model your own stack in the token-cost calculator, or see the Awslabs Valkey policy for what a sensible grant looks like.

//FAQ

Awslabs Valkey token-cost questions.

How many tokens does the Awslabs Valkey MCP server use?+

Its 103 tool definitions total 9,719 tokens — 4.9% of a 200k context window — measured with tiktoken o200k_base over the serialised tools/list payload. Exact counts vary slightly by client and model.

Why does Awslabs Valkey consume tokens before I send a message?+

MCP clients load every connected server's tool definitions — name, description, and input schema — into the model's context so it knows what it can call. That payload is charged against your context window on every request, whether or not a tool is used.

How do I reduce Awslabs Valkey's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Awslabs Valkey to only the tools you allow — ungranted definitions are filtered out of the tool list, so they never enter the context window. A grant of 3 typical tools costs roughly 283 tokens, a 97% reduction.

Does deferred tool loading fix this?+

Partially, in some clients. Claude Code defers MCP tool schemas behind a tool-search step by default, and VS Code has experimental grouping — but you still pay tokens per search and reload, and Cursor, Windsurf and Gemini CLI load definitions upfront. Reducing the exposed tool set cuts the cost in every client.

// METHOD & REVIEW

How these numbers were measured.

01

Serialisation

Each tool is serialised as a tools/list entry — name, description, input schema — from the schemas in the PolicyLayer scan database. Clients differ slightly in framing, so treat counts as close estimates.

02

Tokeniser

tiktoken o200k_base (GPT-4o/o-series). Anthropic's current tokeniser isn't published, so Claude's exact counts will differ; for English text and JSON schemas the totals are close enough to treat these as estimates.

03

Deferred loading

Some clients now defer schema loading (Claude Code's tool search; VS Code experimental grouping). You still pay per search and reload — and Cursor, Windsurf and Gemini CLI load everything upfront.

Computed 07-06-2026 from the PolicyLayer scan database over 103 of 105 catalogued Awslabs Valkey tools. Counts refresh with every site build.