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The Ani MCP server costs 11,236 tokens before the first call.

Connect Ani and its 55 tool definitions are loaded into the model's context on every request — 5.6% of a 200k window spent before your agent does anything.

QUICK ANSWER The Ani MCP server's tool definitions consume 11,236 tokens — 5.9× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 55 tools · 11,236 tokens · 5.6% of 200k · 1.1% 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 5.6%
1M WINDOW 1.1%

Corpus context: Ani ranks #148 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 11,236 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 204 tokens per tool.

ToolCategoryTokens% of server
anilist_pick Execute 526 4.7%
anilist_batch_update Write 429 3.8%
anilist_genres Read 385 3.4%
anilist_search Read 372 3.3%
anilist_list Read 357 3.2%
anilist_seasonal Read 279 2.5%
anilist_explain Read 262 2.3%
anilist_reviews Read 254 2.3%
anilist_group_pick Read 248 2.2%
anilist_details Read 233 2.1%
anilist_seasonal_stats Destructive 231 2.1%
anilist_staff Read 227 2.0%
anilist_update_progress Write 227 2.0%
anilist_staff_search Read 226 2.0%
anilist_feed Read 225 2.0%
anilist_shared_planning Read 218 1.9%
anilist_session Read 214 1.9%
anilist_wrapped Read 206 1.8%
anilist_add_to_list Write 206 1.8%
anilist_export Write 205 1.8%
anilist_trending Read 202 1.8%
anilist_similar Read 201 1.8%
anilist_recommendations Read 198 1.8%
anilist_sequels Read 197 1.8%
anilist_unscored Read 197 1.8%
anilist_completionist Read 195 1.7%
anilist_wrapped_card Read 193 1.7%
anilist_seasonal_recap_card Destructive 192 1.7%
anilist_lookup Read 192 1.7%
anilist_pace Read 192 1.7%
anilist_compat_card Read 191 1.7%
anilist_compare Read 189 1.7%
anilist_characters Read 188 1.7%
anilist_follow_suggestions Read 183 1.6%
anilist_react Read 179 1.6%
anilist_watch_order Read 178 1.6%
anilist_taste Read 169 1.5%
anilist_rate Destructive 168 1.5%
anilist_calibration Read 167 1.5%
anilist_airing Read 161 1.4%
anilist_drops Destructive 159 1.4%
anilist_evolution Read 159 1.4%
anilist_taste_card Read 156 1.4%
anilist_kitsu_import Write 156 1.4%
anilist_genre_list Read 155 1.4%
anilist_mal_import Write 155 1.4%
anilist_studio_search Read 152 1.4%
anilist_favourite Read 140 1.2%
anilist_stats Read 138 1.2%
anilist_delete_from_list Destructive 131 1.2%
anilist_schedule Write 124 1.1%
anilist_profile Read 112 1.0%
anilist_activity Write 97 0.9%
anilist_undo Destructive 75 0.7%
anilist_whoami Read 65 0.6%

Most agents use a handful of these tools. They pay for all 55.

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 (204 tokens each).

Grant scopeDefinition costReduction
All 55 tools (no gateway) 11,236 tokens
3 granted tools ~613 tokens −95%
5 granted tools ~1,021 tokens −91%
10 granted tools ~2,043 tokens −82%

Ani token-cost questions.

How many tokens does the Ani MCP server use?+

Its 55 tool definitions total 11,236 tokens — 5.6% 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 Ani 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 Ani's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Ani 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 613 tokens, a 95% 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 55 catalogued Ani tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Ani 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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