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The Spotify MCP server costs 11,357 tokens before the first call.

Connect Spotify and its 46 tool definitions are loaded into the model's context on every request — 5.7% of a 200k window spent before your agent does anything.

QUICK ANSWER The Spotify MCP server's tool definitions consume 11,357 tokens — 6.0× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 46 tools · 11,357 tokens · 5.7% 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 5.7%
1M WINDOW 1.1%

Corpus context: Spotify ranks #145 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 11,357 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 247 tokens per tool.

ToolCategoryTokens% of server
search.list Read 684 6.0%
playlists.items.list Read 669 5.9%
playlists.items.create Write 638 5.6%
playlists.items.update Read 602 5.3%
playlists.get Read 518 4.6%
artists.albums.list Read 396 3.5%
me.player.play.update Execute 366 3.2%
me.playlists.create Write 348 3.1%
audiobooks.chapters.list Read 328 2.9%
albums.tracks.list Read 318 2.8%
shows.episodes.list Read 318 2.8%
me.player.list Read 310 2.7%
me.player.currently_playing.list Read 305 2.7%
me.albums.list Read 279 2.5%
me.tracks.list Read 279 2.5%
playlists.update Read 279 2.5%
me.episodes.list Read 275 2.4%
playlists.items.delete Destructive 261 2.3%
audiobooks.get Read 243 2.1%
chapters.get Read 242 2.1%
me.top.get Read 231 2.0%
shows.get Read 227 2.0%
tracks.get Read 227 2.0%
episodes.get Read 224 2.0%
albums.get Read 219 1.9%
me.player.update Read 195 1.7%
me.library.contains.list Read 187 1.6%
me.player.recently_played.list Read 181 1.6%
me.library.delete Destructive 170 1.5%
me.library.update Write 170 1.5%
me.player.repeat.update Write 155 1.4%
me.player.seek.update Read 153 1.3%
me.playlists.list Read 131 1.2%
me.player.queue.create Write 131 1.2%
me.player.shuffle.update Read 129 1.1%
me.shows.list Read 129 1.1%
me.player.volume.update Write 125 1.1%
me.audiobooks.list Read 123 1.1%
me.following.list Read 116 1.0%
me.player.previous.create Read 97 0.9%
me.player.next.create Read 95 0.8%
me.player.pause.update Read 92 0.8%
artists.get Read 67 0.6%
me.player.devices.list Read 51 0.4%
me.list Read 38 0.3%
me.player.queue.list Read 36 0.3%

Most agents use a handful of these tools. They pay for all 46.

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 (247 tokens each).

Grant scopeDefinition costReduction
All 46 tools (no gateway) 11,357 tokens
3 granted tools ~741 tokens −93%
5 granted tools ~1,234 tokens −89%
10 granted tools ~2,469 tokens −78%

Spotify token-cost questions.

How many tokens does the Spotify MCP server use?+

Its 46 tool definitions total 11,357 tokens — 5.7% 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 Spotify 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 Spotify's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Spotify 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 741 tokens, a 93% 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 46 catalogued Spotify tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Spotify 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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