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The Pypi:tradememory Protocol MCP server costs 2,929 tokens before the first call.

Connect Pypi:tradememory Protocol and its 15 tool definitions are loaded into the model's context on every request — 1.5% of a 200k window spent before your agent does anything.

QUICK ANSWER The Pypi:tradememory Protocol MCP server's tool definitions consume 2,929 tokens — around the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 15 tools · 2,929 tokens · 1.5% of 200k · 0.3% 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 1.5%
1M WINDOW 0.3%

Corpus context: Pypi:tradememory Protocol ranks #1315 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 2,929 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 195 tokens per tool.

ToolCategoryTokens% of server
remember_trade Read 504 17.2%
store_trade_memory Execute 331 11.3%
recall_memories Read 293 10.0%
create_trading_plan Write 236 8.1%
evolution_evolve_strategy Execute 222 7.6%
evolution_discover_patterns Read 213 7.3%
evolution_run_backtest Execute 205 7.0%
recall_similar_trades Read 170 5.8%
evolution_fetch_market_data Read 158 5.4%
check_active_plans Read 141 4.8%
get_behavioral_analysis Read 122 4.2%
get_strategy_performance Read 115 3.9%
evolution_get_log Read 77 2.6%
get_agent_state Read 71 2.4%
get_trade_reflection Read 71 2.4%

Most agents use a handful of these tools. They pay for all 15.

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 (195 tokens each).

Grant scopeDefinition costReduction
All 15 tools (no gateway) 2,929 tokens
3 granted tools ~586 tokens −80%
5 granted tools ~976 tokens −67%
10 granted tools ~1,953 tokens −33%

Pypi:tradememory Protocol token-cost questions.

How many tokens does the Pypi:tradememory Protocol MCP server use?+

Its 15 tool definitions total 2,929 tokens — 1.5% 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 Pypi:tradememory Protocol 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 Pypi:tradememory Protocol's token usage?+

Expose fewer tools. A PolicyLayer grant scopes Pypi:tradememory Protocol 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 586 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 07-06-2026 from the PolicyLayer scan database over all 15 catalogued Pypi:tradememory Protocol tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes Pypi:tradememory Protocol 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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