Finviz + SEC EDGAR MCP Server

compare_stocks

Compare fundamental metrics across multiple stocks side-by-side using current Finviz snapshot data. Select specific metrics or use default value-investing ratios to evaluate and rank stock fundamentals at a glance.

Instructions

Compare fundamental metrics across multiple stocks side-by-side. Uses Finviz current snapshot data (~15-20 min delayed). All values are point-in-time — for historical comparisons across years use compare_financials (SEC XBRL actuals) instead.

Args: tickers: Comma-separated tickers, e.g. "AAPL,MSFT,GOOGL" metrics: Optional comma-separated metric names to compare. If empty, uses default value-investing metrics: P/E, Forward P/E, P/B, P/FCF, PEG, ROE, ROA, Profit Margin, Oper. Margin, Debt/Eq, Current Ratio, Dividend %, EPS (ttm), EPS next Y, EPS past 5Y, Market Cap, Price

Input Schema

TableJSON Schema

Name	Required	Description	Default
`metrics`	No
`tickers`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Sep 25, 2026

TDQS

A4.7/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses the data source (Finviz), the delay (~15-20 min), and the point-in-time nature of the data. It does not mention side effects, but a compare operation is inherently read-only and the data source disclosure is the most important behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by data freshness and the key alternative. The args section is compact and informative, and every sentence earns its place without redundant filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is complete for this tool's complexity: it covers purpose, data source, temporal scope, default behavior, and the alternative for historical comparisons. Since an output schema exists, the description does not need to explain return values, and nothing critical is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains tickers as comma-separated with an example, and explains metrics as optional comma-separated names with a clear default behavior and the full default metric list. It does not enumerate all possible custom metric names, but it provides enough for effective use.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Compare fundamental metrics across multiple stocks side-by-side.' It clearly distinguishes itself from compare_financials by emphasizing current snapshot data vs. historical SEC XBRL actuals, so an agent can differentiate between them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says to use compare_financials for historical comparisons across years, giving a clear when-not-to-use condition. It also clarifies that all values are point-in-time, which guides when this tool is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.