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The HiveBank MCP server costs 463 tokens before the first call.

Connect HiveBank and its 5 tool definitions are loaded into the model's context on every request — 0.2% of a 200k window spent before your agent does anything.

QUICK ANSWER The HiveBank MCP server's tool definitions consume 463 tokens — below the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 5 tools · 463 tokens · 0.2% of 200k · 0.0% 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 0.2%
1M WINDOW 0.0%

Corpus context: HiveBank ranks #2844 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 463 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 93 tokens per tool.

ToolCategoryTokens% of server
hivebank_create_stream Write 140 30.2%
hivebank_create_vault Write 121 26.1%
hivebank_deposit Financial 101 21.8%
hivebank_get_balance Read 57 12.3%
hivebank_get_stats Read 44 9.5%

Most agents use a handful of these tools. They pay for all 5.

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 (93 tokens each).

Grant scopeDefinition costReduction
All 5 tools (no gateway) 463 tokens
3 granted tools ~278 tokens −40%

HiveBank token-cost questions.

How many tokens does the HiveBank MCP server use?+

Its 5 tool definitions total 463 tokens — 0.2% 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 HiveBank 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 HiveBank's token usage?+

Expose fewer tools. A PolicyLayer grant scopes HiveBank 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 278 tokens, a 40% 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 5 catalogued HiveBank tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes HiveBank 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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