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The Korean Law Search MCP server costs 21,979 tokens before the first call.

Connect Korean Law Search and its 54 tool definitions are loaded into the model's context on every request — 11% of a 200k window spent before your agent does anything.

QUICK ANSWER The Korean Law Search MCP server's tool definitions consume 21,979 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 54 tools · 21,979 tokens · 11% of 200k · 2.2% 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 11%
1M WINDOW 2.2%

Corpus context: Korean Law Search ranks #50 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 21,979 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 407 tokens per tool.

ToolCategoryTokens% of server
ordin_search Read 866 3.9%
prec_search Read 782 3.6%
expc_search Read 728 3.3%
decc_search Read 691 3.1%
admrul_search Read 686 3.1%
cgm_expc_search Read 642 2.9%
law_service Read 636 2.9%
article_citation Read 632 2.9%
eflaw_josub Read 632 2.9%
special_decc_search Read 614 2.8%
trty_search Read 610 2.8%
eflaw_service Read 604 2.7%
detc_search Read 583 2.7%
law_josub Read 581 2.6%
lnkLsOrdJo_search Read 567 2.6%
eflaw_search Read 552 2.5%
elaw_search Read 527 2.4%
committee_search Read 488 2.2%
aiSearch Read 462 2.1%
elaw_service Read 398 1.8%
expc_service Read 383 1.7%
lnkLs_search Read 382 1.7%
law_search Read 381 1.7%
decc_service Read 369 1.7%
chain_amendment_track Execute 357 1.6%
aiRltLs_search Read 353 1.6%
lnkDep_search Read 353 1.6%
admrul_service Read 347 1.6%
prec_service Read 341 1.6%
law_amendment_summary Execute 335 1.5%
lsDelegated_service Read 330 1.5%
check_precedent_odds Read 327 1.5%
article_amendment_diff Read 309 1.4%
simplify_article Read 309 1.4%
ordinLsCon_search Read 307 1.4%
detc_service Read 295 1.3%
legal_resolver Read 291 1.3%
ordin_service Read 286 1.3%
dlytrm_rlt_search Read 274 1.2%
lstrm_rlt_search Read 270 1.2%
jo_rlt_lstrm_search Read 269 1.2%
ls_rlt_search Read 265 1.2%
chain_dispute_prep Read 261 1.2%
lstrm_rlt_jo_search Read 258 1.2%
chain_full_research Read 256 1.2%
dlytrm_search Read 251 1.1%
lstrm_ai_search Read 250 1.1%
chain_law_system Read 223 1.0%
trty_service Read 211 1.0%
cgm_expc_service Read 202 0.9%
special_decc_service Read 199 0.9%
drlaw_search Read 190 0.9%
committee_service Read 189 0.9%
cache_stats Read 75 0.3%

Most agents use a handful of these tools. They pay for all 54.

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 (407 tokens each).

Grant scopeDefinition costReduction
All 54 tools (no gateway) 21,979 tokens
3 granted tools ~1,221 tokens −94%
5 granted tools ~2,035 tokens −91%
10 granted tools ~4,070 tokens −81%

Korean Law Search token-cost questions.

How many tokens does the Korean Law Search MCP server use?+

Its 54 tool definitions total 21,979 tokens — 11% 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 Korean Law Search 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 Korean Law Search's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Korean Law Search 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 1,221 tokens, a 94% 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 54 catalogued Korean Law Search tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Korean Law Search 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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