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The Captapi MCP server costs 6,360 tokens before the first call.

Connect Captapi and its 62 tool definitions are loaded into the model's context on every request — 3.2% of a 200k window spent before your agent does anything.

QUICK ANSWER The Captapi MCP server's tool definitions consume 6,360 tokens — 3.3× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 62 tools · 6,360 tokens · 3.2% of 200k · 0.6% 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 3.2%
1M WINDOW 0.6%

Corpus context: Captapi ranks #943 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 6,360 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 103 tokens per tool.

ToolCategoryTokens% of server
tiktok_comment_replies Write 146 2.3%
youtube_comment_replies Write 143 2.2%
facebook_comment_replies Write 132 2.1%
youtube_summarize Read 122 1.9%
youtube_community_posts Read 120 1.9%
youtube_channel_streams Read 119 1.9%
tiktok_music_posts Read 117 1.8%
youtube_channel_videos Read 117 1.8%
youtube_shorts_summarize Read 117 1.8%
youtube_transcript Read 117 1.8%
youtube_channel_playlists Read 116 1.8%
youtube_channel_shorts Read 116 1.8%
youtube_playlist_videos Read 116 1.8%
tiktok_comments Read 115 1.8%
youtube_shorts_comments Read 115 1.8%
youtube_shorts_transcript Read 115 1.8%
instagram_comments Read 114 1.8%
tiktok_user_followings Read 114 1.8%
tiktok_channel_posts Read 113 1.8%
tiktok_user_followers Read 113 1.8%
instagram_channel_reels Read 112 1.8%
instagram_tagged_posts Read 112 1.8%
youtube_comments Read 112 1.8%
facebook_group_posts Read 111 1.7%
instagram_channel_posts Read 110 1.7%
tiktok_trending_feed Read 110 1.7%
instagram_reels_search Read 109 1.7%
instagram_highlights_details Read 107 1.7%
tiktok_hashtag_search Read 107 1.7%
tiktok_popular_hashtags Read 107 1.7%
tiktok_user_search Read 106 1.7%
youtube_hashtag_search Read 106 1.7%
tiktok_search Read 105 1.7%
tiktok_top_search Read 105 1.7%
facebook_profile_reels Read 104 1.6%
instagram_hashtag_search Read 103 1.6%
facebook_profile_posts Read 102 1.6%
instagram_music_posts Read 102 1.6%
instagram_profile_search Read 101 1.6%
youtube_search Read 101 1.6%
facebook_comments Read 100 1.6%
tiktok_video_download Read 89 1.4%
youtube_channel_details Read 89 1.4%
tiktok_transcript Read 88 1.4%
tiktok_summarize Read 87 1.4%
tiktok_video_details Read 87 1.4%
youtube_shorts_details Read 86 1.4%
tiktok_song_details Read 85 1.3%
youtube_video_details Read 85 1.3%
instagram_embed Read 84 1.3%
tiktok_channel_details Read 84 1.3%
youtube_video_download Read 84 1.3%
instagram_details Read 83 1.3%
instagram_summarize Read 82 1.3%
instagram_video_download Read 82 1.3%
instagram_channel_details Read 81 1.3%
instagram_story_highlights Read 81 1.3%
instagram_transcript Read 80 1.3%
facebook_page_details Read 79 1.2%
facebook_summarize Read 74 1.2%
facebook_details Read 71 1.1%
facebook_transcript Read 70 1.1%

Most agents use a handful of these tools. They pay for all 62.

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 (103 tokens each).

Grant scopeDefinition costReduction
All 62 tools (no gateway) 6,360 tokens
3 granted tools ~308 tokens −95%
5 granted tools ~513 tokens −92%
10 granted tools ~1,026 tokens −84%

Captapi token-cost questions.

How many tokens does the Captapi MCP server use?+

Its 62 tool definitions total 6,360 tokens — 3.2% 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 Captapi 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 Captapi's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Captapi 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 308 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 62 catalogued Captapi tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Captapi 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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