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The SAVORDISH MCP server costs 6,738 tokens before the first call.

Connect SAVORDISH and its 31 tool definitions are loaded into the model's context on every request — 3.4% of a 200k window spent before your agent does anything.

QUICK ANSWER The SAVORDISH MCP server's tool definitions consume 6,738 tokens — 3.5× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 31 tools · 6,738 tokens · 3.4% of 200k · 0.7% of 1M Method →

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 3.4%
1M WINDOW 0.7%

Corpus context: SAVORDISH ranks #915 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.

Where the 6,738 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 217 tokens per tool.

ToolCategoryTokens% of server
nutrition_find_by_macros Read 409 6.1%
nutrition_daily_plan Write 310 4.6%
recipes_search Read 301 4.5%
recipes_get_dietary Read 292 4.3%
meals_plan Write 278 4.1%
shopping_generate_grocery_list Write 273 4.1%
cooking_convert_units Write 266 3.9%
recipes_list_by_cuisine Read 259 3.8%
recipes_find_by_ingredient Read 254 3.8%
nutrition_healthier_swap Read 236 3.5%
cooking_get_substitutions Read 223 3.3%
cooking_get_tips Read 223 3.3%
recipes_get_quick Read 215 3.2%
nutrition_compare Read 211 3.1%
shopping_instacart Read 208 3.1%
nutrition_meal_score Write 204 3.0%
cooking_pair_beverages Read 203 3.0%
recipes_collections Read 202 3.0%
nutrition_analyze Read 194 2.9%
recipes_compare Read 182 2.7%
recipes_get_random Read 180 2.7%
recipes_scale Execute 179 2.7%
platform_get_info Read 179 2.7%
recipes_seasonal Read 172 2.6%
nutrition_ingredient_info Read 171 2.5%
cooking_meal_prep_guide Read 170 2.5%
recipes_get_nutrition Read 167 2.5%
recipes_get Read 166 2.5%
recipes_get_trending Read 153 2.3%
cuisines_explore Read 148 2.2%
account_api_key_status Read 110 1.6%

Most agents use a handful of these tools. They pay for all 31.

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 (217 tokens each).

Grant scopeDefinition costReduction
All 31 tools (no gateway) 6,738 tokens
3 granted tools ~652 tokens −90%
5 granted tools ~1,087 tokens −84%
10 granted tools ~2,174 tokens −68%

SAVORDISH token-cost questions.

How many tokens does the SAVORDISH MCP server use?+

Its 31 tool definitions total 6,738 tokens — 3.4% 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 SAVORDISH 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 SAVORDISH's token usage?+

Expose fewer tools. A PolicyLayer grant scopes SAVORDISH 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 652 tokens, a 90% 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.

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 all 31 catalogued SAVORDISH tools. Counts refresh with every site build.

Expose only the tools you use — the rest never enter your context.

A PolicyLayer grant scopes SAVORDISH to the tools you actually allow. Ungranted definitions never load, and every call that does run is checked against policy first.

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4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

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