Home / Token cost / Mcp Ygoprodeck

The Mcp Ygoprodeck MCP server costs 11,166 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 Mcp Ygoprodeck MCP server's 33 tool definitions consume 11,166 tokens — 5.6% of a 200k context window, and 5.4× 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 #505 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 5.6%
1M WINDOW 1.1%

Corpus context: Mcp Ygoprodeck ranks #505 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 11,166 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 338 tokens per tool.

ToolCategoryTokens% of server
bet_research Read 1,084 9.7%
polymarket_edges Financial 1,070 9.6%
subscribe Write 608 5.4%
polymarket_kalshi_spread Read 588 5.3%
polymarket_arbitrage Financial 581 5.2%
polymarket_fill_risk Write 504 4.5%
ask_pipeworx Read 465 4.2%
deep_research Read 385 3.4%
recent_changes Read 359 3.2%
polymarket_edge_tracker Read 349 3.1%
entity_profile Read 347 3.1%
search_cards Read 333 3.0%
pipeworx_feedback Write 327 2.9%
discover_tools Read 316 2.8%
compare_entities Read 315 2.8%
ask_pipeworx_grounded Read 306 2.7%
ai_visibility_check Read 305 2.7%
resolve_entity Read 289 2.6%
scan_dependency Read 278 2.5%
scan_competitor_ai_presence Read 269 2.4%
search_within Read 268 2.4%
suggest_questions Read 254 2.3%
recent_alerts Read 235 2.1%
validate_claim Read 225 2.0%
pipeworx_trending Read 197 1.8%
generate_llms_txt Write 185 1.7%
remember Write 168 1.5%
recall Read 130 1.2%
get_card Read 116 1.0%
list_subscriptions Read 88 0.8%
forget Destructive 83 0.7%
unsubscribe Write 78 0.7%
random_card Read 61 0.5%

Your agent uses a handful of these tools. It pays for all 33.

You don't need all 33 of those definitions in the window. PolicyLayer is an MCP gateway that sits in front of Mcp Ygoprodeck: 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 33 tools (no gateway) 11,166 tokens
3 granted tools ~1,015 tokens −91%
5 granted tools ~1,692 tokens −85%
10 granted tools ~3,384 tokens −70%

The risk dividend: 3 of these 33 tools are critical-risk (destructive or financial) and cost 1,734 tokens (16% of the definition load). Block them — the recommended starter policy — and you reclaim that context before tuning anything else.

  1. Create a free account and register Mcp Ygoprodeck — 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 MCP YGOPRODECK TOKEN COST →

Instant setup, no code required.

Mcp Ygoprodeck token-cost questions.

How many tokens does the Mcp Ygoprodeck MCP server use?+

Its 33 tool definitions total 11,166 tokens — 5.6% 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 Mcp Ygoprodeck 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 Mcp Ygoprodeck's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Mcp Ygoprodeck 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 1,015 tokens, a 91% 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 33 catalogued Mcp Ygoprodeck tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Mcp Ygoprodeck 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.

// GET IN TOUCH

Have a question or want to learn more? Send us a message.

Message sent.

We'll get back to you soon.