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The Seah Boon Keong - Chat with BNM API Datasets MCP server costs 1,716 tokens before the first call.

Connect Seah Boon Keong - Chat with BNM API Datasets and its 26 tool definitions are loaded into the model's context on every request — 0.9% of a 200k window spent before your agent does anything.

QUICK ANSWER The Seah Boon Keong - Chat with BNM API Datasets MCP server's tool definitions consume 1,716 tokens — around the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 26 tools · 1,716 tokens · 0.9% of 200k · 0.2% 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 0.9%
1M WINDOW 0.2%

Corpus context: Seah Boon Keong - Chat with BNM API Datasets ranks #1695 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 1,716 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 66 tokens per tool.

ToolCategoryTokens% of server
get_exchange_rate Read 176 10.3%
get_currency_circulation_by_denomination Read 101 5.9%
get_usd_myr_intraday_rate Read 99 5.8%
get_usd_myr_reference_rate Read 93 5.4%
get_banking_system_statement_assets Read 87 5.1%
get_base_rate Read 73 4.3%
get_monetary_aggregates_M1_M2_M3 Read 70 4.1%
get_volume_transaction_kl_foreign_exchange_market Read 68 4.0%
get_financial_consumer_alert Read 63 3.7%
get_malaysian_government_securities_market_indicative_yield Read 62 3.6%
get_labour_market_indicators_financial_services_sector Read 61 3.6%
get_volume_transaction_interbank_money_market Read 58 3.4%
get_islamic_interbank_rate Read 55 3.2%
get_daily_FX_turnover Read 53 3.1%
get_interest_rates_banking_institution Read 53 3.1%
get_overnight_policy_rate Read 52 3.0%
get_external_reserves Read 51 3.0%
get_takaful_key_indicators Read 51 3.0%
get_bnm_statement_assets Read 50 2.9%
get_broad_money_M3 Read 50 2.9%
get_malaysia_overnight_rate_i Read 50 2.9%
get_reserve_money Read 50 2.9%
get_bond_market_highlights Read 49 2.9%
get_interbank_swap Read 49 2.9%
get_kijang_emas Read 48 2.8%
get_federal_government_finance Read 44 2.6%

Most agents use a handful of these tools. They pay for all 26.

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 (66 tokens each).

Grant scopeDefinition costReduction
All 26 tools (no gateway) 1,716 tokens
3 granted tools ~198 tokens −88%
5 granted tools ~330 tokens −81%
10 granted tools ~660 tokens −62%

Seah Boon Keong - Chat with BNM API Datasets token-cost questions.

How many tokens does the Seah Boon Keong - Chat with BNM API Datasets MCP server use?+

Its 26 tool definitions total 1,716 tokens — 0.9% 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 Seah Boon Keong - Chat with BNM API Datasets 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 Seah Boon Keong - Chat with BNM API Datasets's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Seah Boon Keong - Chat with BNM API Datasets 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 198 tokens, a 88% 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 26 catalogued Seah Boon Keong - Chat with BNM API Datasets tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Seah Boon Keong - Chat with BNM API Datasets 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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