Home / Token cost / MetricDuck — Financial Analysis

The MetricDuck — Financial Analysis MCP server costs 23,482 tokens before the first call.

Connect MetricDuck — Financial Analysis and its 22 tool definitions are loaded into the model's context on every request — 12% of a 200k window spent before your agent does anything.

QUICK ANSWER The MetricDuck — Financial Analysis MCP server's tool definitions consume 23,482 tokens — 12× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 22 tools · 23,482 tokens · 12% of 200k · 2.3% 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 12%
1M WINDOW 2.3%

Corpus context: MetricDuck — Financial Analysis ranks #44 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 23,482 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 1,067 tokens per tool.

ToolCategoryTokens% of server
screen_filing_signals Execute 4,553 19.4%
get_filing_section Read 3,047 13.0%
get_metric_history Read 1,778 7.6%
search_sec_filings Read 1,416 6.0%
screen_companies Write 1,356 5.8%
browse_signal Read 1,245 5.3%
compare_earnings_calls Execute 1,173 5.0%
list_filings Read 1,158 4.9%
get_filing_index Read 1,019 4.3%
get_stock_price Read 857 3.6%
compare_companies Read 733 3.1%
get_xbrl_facts Read 658 2.8%
search_transcripts Read 646 2.8%
list_recent_filings Read 608 2.6%
search_filings Read 604 2.6%
get_company_overview Read 471 2.0%
get_financials Read 427 1.8%
get_guidance_vs_actual Read 422 1.8%
browse_company Read 396 1.7%
search_companies Read 334 1.4%
get_company_facts Read 319 1.4%
get_financial_metrics Read 262 1.1%

Most agents use a handful of these tools. They pay for all 22.

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 (1,067 tokens each).

Grant scopeDefinition costReduction
All 22 tools (no gateway) 23,482 tokens
3 granted tools ~3,202 tokens −86%
5 granted tools ~5,337 tokens −77%
10 granted tools ~10,674 tokens −55%

MetricDuck — Financial Analysis token-cost questions.

How many tokens does the MetricDuck — Financial Analysis MCP server use?+

Its 22 tool definitions total 23,482 tokens — 12% 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 MetricDuck — Financial Analysis 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 MetricDuck — Financial Analysis's token usage?+

Expose fewer tools. A PolicyLayer grant scopes MetricDuck — Financial Analysis 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 3,202 tokens, a 86% 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 22 catalogued MetricDuck — Financial Analysis tools. Counts refresh with every site build.

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

A PolicyLayer grant scopes MetricDuck — Financial Analysis 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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