// MCP TOKEN COST

The AI 代理人任務中樞 MCP server costs 18,113 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 AI 代理人任務中樞 MCP server's 174 tool definitions consume 18,113 tokens — 9.1% of a 200k context window, and 9.7× the median MCP server (1,862 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS tiktoken o200k_base · rank #1066 of 12,722 measured servers · refreshed every build Method →

// CONTEXT WINDOW SHARE

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 9.1%

1M WINDOW 1.8%

Corpus context: AI 代理人任務中樞 ranks #1066 of 12,722 measured MCP servers by definition cost. The median is 1,862 tokens, p90 is 13,674, and the heaviest (Ainumbers Mcp Apps) is 442,047 — 221% of a 200k window on its own. New to this? See MCP token cost and context window in the glossary.

Token cost is one axis. See the risk picture across your whole stack →

// PER-TOOL BREAKDOWN

Where the 18,113 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 104 tokens per tool.

Tool	Category	Tokens	% of server
voices__get_topic_opinions	Read	391	2.2%
toys__search_toys	Read	301	1.7%
wellness__check_telehealth_eligibility	Read	228	1.3%
games__official_game_trailers	Read	215	1.2%
creator__calc_group_buy_profit	Read	213	1.2%
stocks__get_tw_shareholder_meetings	Read	211	1.2%
sports__list_sports_events	Read	193	1.1%
crypto__estimate_overseas_crypto_tax	Read	190	1.0%
toys__list_trades	Read	181	1.0%
toys__release_calendar	Read	175	1.0%
health__check_cancer_screening_eligibility	Read	171	0.9%
models__list_ai_models	Read	166	0.9%
toys__global_toy_calendar	Read	164	0.9%
stocks__get_us_stock	Read	155	0.9%
hub_search_mcp_servers	Read	154	0.9%
screen__list_titles	Read	151	0.8%
health__get_drug_recalls	Read	150	0.8%
games__list_games	Read	149	0.8%
seo__geo_citation_audit	Read	148	0.8%
life__find_subsidies	Read	145	0.8%
voices__search_human_opinions	Read	144	0.8%
life__check_lottery	Read	143	0.8%
stocks__get_tw_ex_dividend_yield	Read	139	0.8%
seo__build_search_urls	Execute	138	0.8%
stocks__get_tw_market_quotes	Read	137	0.8%
aitools__estimate_ai_api_cost_twd	Read	134	0.7%
glossary__explain_term	Read	134	0.7%
stocks__get_theme_stocks	Read	131	0.7%
travel__check_destination_crowd	Read	131	0.7%
glossary__get_howto	Read	128	0.7%
shop__search_products	Read	128	0.7%
stocks__get_us_quotes	Read	128	0.7%
riftbound__get_chapter	Read	127	0.7%
motion__generate_keyframes	Write	127	0.7%
trip__convert_currency	Write	126	0.7%
toys__official_toy_videos	Read	125	0.7%
screen__get_cinema_showtimes	Read	124	0.7%
seo__youtube_seo_check	Read	122	0.7%
motion__generate_easing	Write	122	0.7%
games__game_hype_ranking	Read	120	0.7%
life__get_water_outages	Read	118	0.7%
seo__indexnow_submit	Write	117	0.6%
games__open_game_lfg	Write	116	0.6%
life__check_invoice	Read	114	0.6%
models__get_ai_model	Read	114	0.6%
motion__web_video_ffmpeg	Read	114	0.6%
screen__get_title	Read	114	0.6%
games__open_game_trades	Write	114	0.6%
seo__generate_robots_txt	Write	114	0.6%
creator__check_ad_claims	Read	113	0.6%
llms__check_llms_txt	Read	113	0.6%
glossary__compare_concepts	Read	112	0.6%
wellness__score_mood_thermometer	Read	112	0.6%
creator__check_tax_registration	Read	111	0.6%
life__get_work_school_closures	Read	111	0.6%
travel__flight_booking_timing	Read	111	0.6%
crypto__get_usdt_twd_premium	Read	109	0.6%
seo__generate_meta_tags	Write	109	0.6%
life__get_power_outages	Read	108	0.6%
health__check_home_blood_pressure	Read	107	0.6%
shop__list_chain_promos	Read	107	0.6%
stocks__estimate_etf_dividend_2027	Read	106	0.6%
stocks__get_tw_material_news	Read	106	0.6%
games__get_game	Read	105	0.6%
health__get_glp1_side_effects	Read	105	0.6%
health__get_food_ad_violations	Read	104	0.6%
motion__generate_gradient	Write	104	0.6%
stocks__get_tw_margin_chips	Read	103	0.6%
creator__calc_creator_withholding	Read	102	0.6%
crypto__check_scam_domain	Read	102	0.6%
glossary__classify_search_intent	Read	102	0.6%
health__check_blood_glucose	Read	102	0.6%
life__get_lottery_results	Read	102	0.6%
trip__domestic_travel	Read	102	0.6%
sports__get_sports_event	Read	101	0.6%
health__check_meal_cancer_risk	Read	100	0.6%
stocks__get_tw_dividend	Read	100	0.6%
wellness__calc_sleep_times	Read	100	0.6%
stocks__get_tw_ex_dividend	Read	99	0.5%
trip__entry_requirements	Read	99	0.5%
trip__exchange_rates	Read	99	0.5%
aitools__get_taiwan_ai_guidance	Read	98	0.5%
aitools__compare_ai_subscriptions	Read	97	0.5%
glossary__list_topics	Read	97	0.5%
seo__verify_crawler_ip	Read	97	0.5%
stocks__get_etf_dividends	Read	97	0.5%
trip__esim_estimate	Read	97	0.5%
wellness__score_phq9	Read	97	0.5%
life__get_holidays	Read	96	0.5%
toys__official_toy_news	Read	96	0.5%
hub_find_tool_for_task	Read	95	0.5%
seo__search_operators	Read	95	0.5%
aitools__compare_agent_platforms	Read	93	0.5%
aitools__get_ai_api_pricing	Read	93	0.5%
llms__check_ai_crawler_access	Read	93	0.5%
screen__list_cinemas	Read	93	0.5%
stocks__list_stock_themes	Read	93	0.5%
riftbound__search_rules	Read	92	0.5%
seo__check_site_crawlers	Read	92	0.5%
trip__destination_info	Read	92	0.5%
games__steam_wishlist_upcoming	Read	91	0.5%
motion__animation_a11y_checklist	Read	91	0.5%
motion__spring_to_css_linear	Read	91	0.5%
life__get_invoice_numbers	Read	90	0.5%
seo__ai_crawler_list	Read	90	0.5%
twostroke__get_model_specs	Read	90	0.5%
twostroke__search_models	Read	90	0.5%
health__get_international_food_alerts	Read	88	0.5%
screen__upcoming_showtimes_in_city	Read	88	0.5%
shop__list_price_drops	Read	88	0.5%
wellness__score_gad7	Read	88	0.5%
seo__test_robots_txt	Read	87	0.5%
stocks__get_stock	Read	87	0.5%
travel__get_2027_long_weekends	Read	87	0.5%
voices__check_ai_text	Read	87	0.5%
travel__plan_leave_2027	Write	87	0.5%
health__get_counseling_guide	Read	86	0.5%
seo__serp_preview	Read	86	0.5%
shop__get_product_price	Read	86	0.5%
voices__list_taiwan_posts	Read	86	0.5%
health__check_product_safety	Read	84	0.5%
creator__lookup_supplier_company	Read	83	0.5%
glossary__search_glossary	Read	83	0.5%
sports__whats_on	Read	83	0.5%
life__get_fuel_prices	Read	82	0.5%
screen__lookup_any_title	Read	82	0.5%
crypto__list_registered_vasps	Read	81	0.4%
models__check_code_for_deprecated_models	Read	81	0.4%
life__get_fuel_promos	Read	80	0.4%
twostroke__get_maintenance	Read	80	0.4%
toys__latest_toy_news	Read	78	0.4%
games__lookup_any_game	Read	76	0.4%
sports__lookup_any_sports_event	Read	76	0.4%
travel__ticket_booking_guide	Read	76	0.4%
shop__compare_chain_prices	Read	75	0.4%
wellness__breathing_exercises	Read	74	0.4%
life__get_next_long_weekend	Read	73	0.4%
motion__compare_animation_libraries	Read	73	0.4%
wellness__digital_wellness_guide	Read	73	0.4%
twostroke__find_part	Read	71	0.4%
toys__get_toy	Read	70	0.4%
creator__endorsement_rules	Read	69	0.4%
llms__validate_llms_txt	Read	69	0.4%
models__upcoming_ai_model_shutdowns	Read	69	0.4%
riftbound__lookup_term	Read	69	0.4%
creator__platform_fees	Read	68	0.4%
games__release_calendar	Read	68	0.4%
life__is_holiday	Read	68	0.4%
screen__release_countdown	Read	68	0.4%
seo__search_submit_guide	Read	68	0.4%
seo__search_engine_market_share	Read	67	0.4%
sports__taiwan_broadcast	Read	67	0.4%
motion__animation_browser_support	Read	66	0.4%
screen__release_calendar	Read	66	0.4%
trip__ghibli_park_tickets	Read	66	0.4%
wellness__crisis_lines	Read	66	0.4%
creator__listing_checklist	Read	65	0.4%
hub_search_x402_apis	Read	65	0.4%
sports__sports_calendar	Read	65	0.4%
sports__event_countdown	Read	63	0.3%
trip__japan_taxfree_rules	Read	63	0.3%
games__delay_history	Read	62	0.3%
riftbound__get_banlist	Read	61	0.3%
llms__generate_llms_txt	Write	61	0.3%
models__find_ai_model_replacement	Read	60	0.3%
games__release_countdown	Read	59	0.3%
shop__list_lowest_prices	Read	59	0.3%
screen__schedule_changes	Write	59	0.3%
models__ai_model_shutdown_calendar	Read	55	0.3%
trip__taiwan_holidays	Read	55	0.3%
voices__get_taiwan_post	Read	53	0.3%
riftbound__get_faq	Read	47	0.3%
toys__toy_stats	Read	47	0.3%
shop__list_categories	Read	39	0.2%

// CUT THE COST

Your agent uses a handful of these tools. It pays for all 174.

You don't need all 174 of those definitions in the window. PolicyLayer is an MCP gateway that sits in front of AI 代理人任務中樞: 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 scope	Definition cost	Reduction
All 174 tools (no gateway)	18,113 tokens	—
3 granted tools	~312 tokens	−98%
5 granted tools	~520 tokens	−97%
10 granted tools	~1,041 tokens	−94%

Create a free account and register AI 代理人任務中樞 — nothing to install.
Grant only the tools you use — ungranted definitions never enter the context window.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

CUT AI 代理人任務中樞 TOKEN COST →

Instant setup, no code required.

Model your own stack in the token-cost calculator, or see the AI 代理人任務中樞 policy for what a sensible grant looks like.

//FAQ

AI 代理人任務中樞 token-cost questions.

How many tokens does the AI 代理人任務中樞 MCP server use?+

Its 174 tool definitions total 18,113 tokens — 9.1% 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 AI 代理人任務中樞 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 AI 代理人任務中樞's token usage?+

Expose fewer tools. A PolicyLayer grant scopes AI 代理人任務中樞 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 312 tokens, a 98% 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.

// METHOD & REVIEW

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 20-10-2026 from the PolicyLayer scan database over all 174 catalogued AI 代理人任務中樞 tools. Counts refresh with every site build.