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The Helm MCP server costs 922 tokens before the first call.

Connect Helm and its 5 tool definitions are loaded into the model's context on every request — 0.5% of a 200k window spent before your agent does anything.

QUICK ANSWER The Helm MCP MCP server's tool definitions consume 922 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 5 tools · 922 tokens · 0.5% 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 0.5%
1M WINDOW 0.1%

Corpus context: Helm ranks #2264 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 922 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 184 tokens per tool.

ToolCategoryTokens% of server
get_values Read 308 33.4%
search_charts Read 182 19.7%
get_versions Read 149 16.2%
get_notes Read 144 15.6%
get_dependencies Read 139 15.1%

Most agents use a handful of these tools. They pay for all 5.

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 (184 tokens each).

Grant scopeDefinition costReduction
All 5 tools (no gateway) 922 tokens
3 granted tools ~553 tokens −40%

Helm MCP token-cost questions.

How many tokens does the Helm MCP server use?+

Its 5 tool definitions total 922 tokens — 0.5% 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 Helm MCP 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 Helm MCP's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Helm MCP 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 553 tokens, a 40% 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 5 catalogued Helm MCP tools. Counts refresh with every site build.

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

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

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4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

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