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The Cure Cancer With AI MCP server costs 2,373 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 Cure Cancer With AI MCP server's 15 tool definitions consume 2,373 tokens — 1.2% of a 200k context window, and around the median MCP server (1,897 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS tiktoken o200k_base · rank #3743 of 8,458 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 1.2%
1M WINDOW 0.2%

Corpus context: Cure Cancer With AI ranks #3743 of 8,458 measured MCP servers by definition cost. The median is 1,897 tokens, p90 is 12,637, and the heaviest (Ainumbers Mcp Apps) is 330,550 — 165% of a 200k window on its own. New to this? See MCP token cost and context window in the glossary.

Where the 2,373 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 158 tokens per tool.

ToolCategoryTokens% of server
search_compounds Read 261 11.0%
list_research Read 247 10.4%
list_fda_approvals Read 230 9.7%
search_oncology Read 221 9.3%
list_news Read 217 9.1%
list_clinical_trials Read 178 7.5%
list_blog_posts Read 170 7.2%
predict_dti Execute 163 6.9%
predict_ppi Execute 158 6.7%
predict_clintox Read 117 4.9%
get_clinical_trial Read 97 4.1%
get_research_paper Read 93 3.9%
get_blog_post Read 83 3.5%
mammal_health Read 70 2.9%
list_compound_characteristics Read 68 2.9%

Your agent uses a handful of these tools. It pays for all 15.

You don't need all 15 of those definitions in the window. PolicyLayer is an MCP gateway that sits in front of Cure Cancer With AI: 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 15 tools (no gateway) 2,373 tokens
3 granted tools ~475 tokens −80%
5 granted tools ~791 tokens −67%
10 granted tools ~1,582 tokens −33%
  1. Create a free account and register Cure Cancer With AI — 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 CURE CANCER WITH AI TOKEN COST →

Instant setup, no code required.

Cure Cancer With AI token-cost questions.

How many tokens does the Cure Cancer With AI MCP server use?+

Its 15 tool definitions total 2,373 tokens — 1.2% 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 Cure Cancer With AI 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 Cure Cancer With AI's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Cure Cancer With AI 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 475 tokens, a 80% 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 10-09-2026 from the PolicyLayer scan database over all 15 catalogued Cure Cancer With AI tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Cure Cancer With AI 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.

46,500+ MCP servers and 515,000+ tools scanned and risk-classified.

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