// MCP TOKEN COST

The Pipiads MCP server costs 16,167 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 Pipiads MCP server's 73 tool definitions consume 16,167 tokens — 8.1% of a 200k context window, and 7.8× 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 #94 of 3,354 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 8.1%

1M WINDOW 1.6%

Corpus context: Pipiads ranks #94 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.

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

// PER-TOOL BREAKDOWN

Where the 16,167 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 221 tokens per tool.

Tool	Category	Tokens	% of server
search_ads	Read	1,051	6.5%
search_advertisers	Read	823	5.1%
search_tiktok_products	Read	791	4.9%
search_products	Read	768	4.8%
search_app_developers	Read	721	4.5%
search_stores	Read	714	4.4%
search_apps	Read	700	4.3%
search_tiktok_shops	Read	696	4.3%
get_monitor_ad_list	Read	555	3.4%
search_lib_ads	Read	523	3.2%
search_adlibrary_products	Read	486	3.0%
get_app_ranking	Read	451	2.8%
search_natural_videos	Read	450	2.8%
get_app_dev_ranking	Read	433	2.7%
get_new_product_ranking	Read	364	2.3%
get_monitor_product_list	Read	356	2.2%
get_new_app_ranking	Read	351	2.2%
get_product_ranking	Read	319	2.0%
ai_search_image_ads	Read	300	1.9%
get_monitor_landing_page_list	Read	300	1.9%
ai_search_image_products	Read	240	1.5%
save_monitor_notifications	Write	206	1.3%
ai_search_image_stores	Read	172	1.1%
ai_search_image_tiktok_products	Read	164	1.0%
ai_search_image_tiktok_shops	Read	164	1.0%
list_monitor_tasks	Read	142	0.9%
get_store_play_cost	Read	139	0.9%
get_monitor_longest_run_copy	Read	127	0.8%
get_monitor_most_used_copy	Read	127	0.8%
get_store_longest_run_ads	Read	126	0.8%
get_store_most_used_ads	Read	126	0.8%
get_store_ad_copy_analysis	Read	125	0.8%
get_store_ad_trend	Read	113	0.7%
get_store_ad_schedule	Read	108	0.7%
get_store_data_analysis	Read	105	0.6%
create_monitor_task	Write	103	0.6%
get_monitor_landing_pages_overview	Read	97	0.6%
ai_search_submit_image	Read	96	0.6%
get_monitor_ad_count_stats	Read	95	0.6%
get_monitor_product_stats	Read	95	0.6%
get_store_product_analysis	Read	95	0.6%
get_store_fb_pages	Read	93	0.6%
get_ad_detail	Read	87	0.5%
set_monitor_task_group	Write	87	0.5%
get_monitor_daily_overview	Read	85	0.5%
get_product_detail	Read	85	0.5%
get_monitor_deep_analysis	Read	83	0.5%
get_store_detail	Read	83	0.5%
get_monitor_latest_products	Read	82	0.5%
get_tiktok_product_detail	Read	80	0.5%
update_monitor_group	Write	80	0.5%
get_advertiser_detail	Read	77	0.5%
get_adlibrary_product_detail	Read	75	0.5%
search_fb_advertisers	Read	72	0.4%
ai_search_image_status	Read	70	0.4%
ai_search_image_summary	Read	70	0.4%
get_monitor_realtime_overview	Read	70	0.4%
get_monitor_ad_detail	Read	69	0.4%
get_tiktok_shop_detail	Read	69	0.4%
get_lib_ad_detail	Read	67	0.4%
get_monitor_task_detail	Read	67	0.4%
cancel_monitor_task	Destructive	66	0.4%
get_store_delivery_analysis	Read	66	0.4%
get_app_developer_detail	Read	65	0.4%
get_store_competition	Read	65	0.4%
delete_monitor_group	Destructive	64	0.4%
get_store_region_analysis	Read	63	0.4%
get_store_rank_data	Read	62	0.4%
create_monitor_group	Write	62	0.4%
get_app_detail	Read	61	0.4%
get_monitor_board	Read	42	0.3%
list_monitor_groups	Read	42	0.3%
get_monitor_notifications	Read	41	0.3%

// CUT THE COST

Your agent uses a handful of these tools. It pays for all 73.

You don't need all 73 of those definitions in the window. PolicyLayer is an MCP gateway that sits in front of Pipiads: 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 73 tools (no gateway)	16,167 tokens	—
3 granted tools	~664 tokens	−96%
5 granted tools	~1,107 tokens	−93%
10 granted tools	~2,215 tokens	−86%

The risk dividend: 2 of these 73 tools are critical-risk (destructive or financial) and cost 130 tokens (1% of the definition load). Block them — the recommended starter policy — and you reclaim that context before tuning anything else.

Create a free account and register Pipiads — 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 PIPIADS TOKEN COST →

Instant setup, no code required.

Model your own stack in the token-cost calculator, or see the Pipiads policy for what a sensible grant looks like.

//FAQ

Pipiads token-cost questions.

How many tokens does the Pipiads MCP server use?+

Its 73 tool definitions total 16,167 tokens — 8.1% 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 Pipiads 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 Pipiads's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Pipiads 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 664 tokens, a 96% 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.

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.

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.

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 73 catalogued Pipiads tools. Counts refresh with every site build.

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

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

CUT PIPIADS TOKEN COST →

Instant setup, no code required.

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