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The Raven MCP server costs 7,392 tokens before the first call.

Connect Raven and its 32 tool definitions are loaded into the model's context on every request — 3.7% of a 200k window spent before your agent does anything.

QUICK ANSWER The Raven MCP server's tool definitions consume 7,392 tokens — 3.9× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 32 tools · 7,392 tokens · 3.7% 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 3.7%
1M WINDOW 0.7%

Corpus context: Raven ranks #806 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 7,392 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 231 tokens per tool.

ToolCategoryTokens% of server
generate_service_blueprint Write 879 11.9%
audit_screen Read 535 7.2%
audit_ios_screen Read 496 6.7%
generate_design_system Write 353 4.8%
audit_ios_privacy Write 343 4.6%
audit_rn Read 320 4.3%
audit_layout Read 312 4.2%
get_principles Read 310 4.2%
audit_swiftui Read 288 3.9%
get_pattern Read 226 3.1%
get_brand_system Read 218 2.9%
get_checklist Read 200 2.7%
get_brand_principles Read 192 2.6%
get_design_system Read 184 2.5%
audit_page Execute 183 2.5%
get_research_method Read 178 2.4%
raven_reflect Read 178 2.4%
get_content_principles Read 174 2.4%
compose_system Write 169 2.3%
get_metrics_framework Read 168 2.3%
get_content_system Read 166 2.2%
get_service_pattern Read 150 2.0%
evaluate_design Read 141 1.9%
search_knowledge Read 139 1.9%
get_d4d_framework Read 123 1.7%
list_content_systems Read 123 1.7%
get_business_strategy Read 120 1.6%
get_content_pattern Read 111 1.5%
raven_register Write 111 1.5%
get_brand_trends Read 108 1.5%
list_design_systems Read 108 1.5%
get_service_standard Read 86 1.2%

Most agents use a handful of these tools. They pay for all 32.

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 (231 tokens each).

Grant scopeDefinition costReduction
All 32 tools (no gateway) 7,392 tokens
3 granted tools ~693 tokens −91%
5 granted tools ~1,155 tokens −84%
10 granted tools ~2,310 tokens −69%

Raven token-cost questions.

How many tokens does the Raven MCP server use?+

Its 32 tool definitions total 7,392 tokens — 3.7% 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 Raven 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 Raven's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Raven 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 693 tokens, a 91% 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 32 catalogued Raven tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Raven 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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