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The Parse MCP server costs 349 tokens before the first call.

Connect Parse and its 6 tool definitions are loaded into the model's context on every request — 0.2% of a 200k window spent before your agent does anything.

QUICK ANSWER The Parse MCP server's tool definitions consume 349 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 · 349 tokens · 0.2% of 200k · 0.0% 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 0.2%
1M WINDOW 0.0%

Corpus context: Parse ranks #3005 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 349 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 58 tokens per tool.

ToolCategoryTokens% of server
parse_search Execute 101 28.9%
parse_get_brand Execute 60 17.2%
search Read 53 15.2%
fetch Read 48 13.8%
parse_get_prompt Execute 47 13.5%
parse_get_stats Execute 40 11.5%

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 (58 tokens each).

Grant scopeDefinition costReduction
All 6 tools (no gateway) 349 tokens
3 granted tools ~175 tokens −50%
5 granted tools ~291 tokens −17%

Parse token-cost questions.

How many tokens does the Parse MCP server use?+

Its 6 tool definitions total 349 tokens — 0.2% 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 Parse 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 Parse's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Parse 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 175 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 Parse tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Parse 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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