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The Taiwan Payroll MCP server costs 3,938 tokens before the first call.

Every request your agent makes carries every tool definition this server exposes — context your code, documents and conversation can't use, mostly for tools the agent never calls. You don't need them all in the window, and you don't have to pay for them.

QUICK ANSWER The Taiwan Payroll MCP server's 9 tool definitions consume 3,938 tokens — 2.0% of a 200k context window, and around the median MCP server (2,069 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS tiktoken o200k_base · rank #1250 of 3,354 measured servers · refreshed every build Method →

What that costs 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 2.0%
1M WINDOW 0.4%

Corpus context: Taiwan Payroll ranks #1250 of 3,354 measured MCP servers by definition cost. The median is 2,069 tokens, p90 is 11,359, and the heaviest (SmartBear MCP) is 137,725 — 69% of a 200k window on its own. New to this? See MCP token cost and context window in the glossary.

Where the 3,938 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 438 tokens per tool.

ToolCategoryTokens% of server
calculate_prorated Read 748 19.0%
calculate_payroll Read 686 17.4%
calculate_old_age_single_payment Read 492 12.5%
calculate_supplementary_premium Read 450 11.4%
calculate_old_age_lump_sum Read 380 9.6%
calculate_old_age_pension Read 380 9.6%
calculate_income_tax_withholding Read 370 9.4%
calculate_employer_supplementary_premium Read 360 9.1%
list_years Read 72 1.8%

Your agent uses a handful of these tools. It pays for all 9.

You don't need all 9 of those definitions in the window. PolicyLayer is an MCP gateway that sits in front of Taiwan Payroll: only the tools you grant are exposed to the agent, the rest never load. A smaller window means a sharper agent — less noise when it picks a tool — and every request costs less:

Grant scopeDefinition costReduction
All 9 tools (no gateway) 3,938 tokens
3 granted tools ~1,313 tokens −67%
5 granted tools ~2,188 tokens −44%
  1. Create a free account and register Taiwan Payroll — nothing to install.
  2. Grant only the tools you use — ungranted definitions never enter the context window.
  3. Point your MCP client (Claude, Cursor, anything) at your gateway URL.
CUT TAIWAN PAYROLL TOKEN COST →

Instant setup, no code required.

Taiwan Payroll token-cost questions.

How many tokens does the Taiwan Payroll MCP server use?+

Its 9 tool definitions total 3,938 tokens — 2.0% 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 Taiwan Payroll 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 Taiwan Payroll's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Taiwan Payroll 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 1,313 tokens, a 67% 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 06-07-2026 from the PolicyLayer scan database over all 9 catalogued Taiwan Payroll tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Taiwan Payroll to the tools you actually allow. Ungranted definitions never load, and every call that does run is checked against policy first.

Instant setup, no code required.

43,000+ MCP servers and 220,000+ tools scanned and risk-classified.

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