Home / Token cost / Qmd

The Qmd MCP server costs 850 tokens before the first call.

Connect Qmd and its 6 tool definitions are loaded into the model's context on every request — 0.4% of a 200k window spent before your agent does anything.

QUICK ANSWER The Qmd MCP server's tool definitions consume 850 tokens — below the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 6 tools · 850 tokens · 0.4% of 200k · 0.1% 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 0.4%
1M WINDOW 0.1%

Corpus context: Qmd ranks #2348 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 850 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 142 tokens per tool.

ToolCategoryTokens% of server
get Read 186 21.9%
multi_get Read 165 19.4%
vector_search Read 156 18.4%
deep_search Read 153 18.0%
search Read 138 16.2%
status Read 52 6.1%

Most agents use a handful of these tools. They pay for all 6.

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 (142 tokens each).

Grant scopeDefinition costReduction
All 6 tools (no gateway) 850 tokens
3 granted tools ~425 tokens −50%
5 granted tools ~708 tokens −17%

Qmd token-cost questions.

How many tokens does the Qmd MCP server use?+

Its 6 tool definitions total 850 tokens — 0.4% 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 Qmd 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 Qmd's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Qmd 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 425 tokens, a 50% 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 6 catalogued Qmd tools. Counts refresh with every site build.

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

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

Free to start. No card required.

4,600+ MCP servers and 31,000+ 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.