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The Saber MCP server costs 23,670 tokens before the first call.

Connect Saber and its 77 tool definitions are loaded into the model's context on every request — 12% of a 200k window spent before your agent does anything.

QUICK ANSWER The Saber MCP server's tool definitions consume 23,670 tokens — 12× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 77 tools · 23,670 tokens · 12% of 200k · 2.4% 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 12%
1M WINDOW 2.4%

Corpus context: Saber ranks #41 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 23,670 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 307 tokens per tool.

ToolCategoryTokens% of server
company_signals-create Write 1,463 6.2%
market_signals-create_subscription Write 1,099 4.6%
company_signals-create_batch Write 1,042 4.4%
contacts-create_signal Write 895 3.8%
company_signals-list Read 799 3.4%
scoring-rule-upsert Write 757 3.2%
signal_subscriptions-create Write 747 3.2%
contacts-search Read 724 3.1%
company_lists-update Write 716 3.0%
company_signals-subscription_logs Read 709 3.0%
company_lists-create Write 706 3.0%
company_lists-count_preview Read 660 2.8%
company_lists-search Read 658 2.8%
signal_subscriptions-update Write 500 2.1%
signal_templates-create Write 498 2.1%
contacts-create_research Write 488 2.1%
signal_templates-update Write 464 2.0%
company_lists-export Write 460 1.9%
company_lists-import Write 386 1.6%
market_signals-update_subscription Write 366 1.5%
subscription_actions-update Write 364 1.5%
contact_lists-create Write 295 1.2%
findEmail Read 283 1.2%
subscription_actions-unpause Read 283 1.2%
subscription_actions-pause Read 281 1.2%
subscription_actions-delete Destructive 280 1.2%
subscription_actions-get Read 280 1.2%
scoring-assignment-bulk-create Write 280 1.2%
scoring-rule-delete Destructive 278 1.2%
subscription_actions-create Write 275 1.2%
organisation-update Write 255 1.1%
market_signals-list_signals Read 244 1.0%
scoring-assignment-create Write 244 1.0%
scoring-profile-update Write 230 1.0%
subscription_actions-list Read 225 1.0%
signal_summaries-list Read 217 0.9%
signal_summaries-generate Write 200 0.8%
signal_templates-delete Destructive 176 0.7%
market_signals-trigger_subscription Execute 170 0.7%
signal_subscriptions-trigger Execute 168 0.7%
market_signals-resume_subscription Write 168 0.7%
market_signals-pause_subscription Read 167 0.7%
market_signals-delete_subscription Destructive 166 0.7%
market_signals-get_subscription Read 166 0.7%
signal_subscriptions-start Execute 164 0.7%
signal_subscriptions-stop Execute 164 0.7%
signal_subscriptions-get Read 164 0.7%
signal_templates-get Read 163 0.7%
scoring-rule-list Read 162 0.7%
scoring-assignment-delete Destructive 160 0.7%
scoring-profile-delete Destructive 160 0.7%
scoring-profile-get Read 160 0.7%
scoring-compute Execute 158 0.7%
contact_lists-get_contacts Read 150 0.6%
company_lists-get_companies Read 146 0.6%
market_signals-list_subscriptions Read 144 0.6%
getContactResearchByExternalID Read 142 0.6%
contacts-list_signals Read 139 0.6%
contact_lists-update Write 129 0.5%
scoring-profile-create Write 129 0.5%
company_signals-get Read 126 0.5%
contacts-get_signal Read 126 0.5%
signal_templates-list Read 124 0.5%
scoring-assignment-list Read 117 0.5%
company_lists-list Read 116 0.5%
contact_lists-list Read 116 0.5%
scoring-scores-get Read 106 0.4%
signal_subscriptions-list Read 104 0.4%
contacts-get_research Read 75 0.3%
company_lists-delete Destructive 71 0.3%
contact_lists-delete Destructive 70 0.3%
company_lists-get Read 70 0.3%
contact_lists-get Read 67 0.3%
credits-get_balance Read 37 0.2%
connectors-list Read 28 0.1%
scoring-profile-list Read 26 0.1%
organisation-get Read 25 0.1%

Most agents use a handful of these tools. They pay for all 77.

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 (307 tokens each).

Grant scopeDefinition costReduction
All 77 tools (no gateway) 23,670 tokens
3 granted tools ~922 tokens −96%
5 granted tools ~1,537 tokens −94%
10 granted tools ~3,074 tokens −87%

Saber token-cost questions.

How many tokens does the Saber MCP server use?+

Its 77 tool definitions total 23,670 tokens — 12% 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 Saber 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 Saber's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Saber 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 922 tokens, a 96% 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 77 catalogued Saber tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Saber 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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