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Edgar & Edgarette: SEC Filing Boards

get_cash_leaders

This week’s cash leaders from audited SEC filings: the top 20 US companies ($2B+) by cash returned to owners (dividends + net buybacks ÷ market value), by operating cash earned ÷ market value, and by expected return among steady earners (income yield + Edgarette’s 80% revenue range). Also short-term places (Treasury-bill, floating-rate and ultra-short funds from their N-PORT filings: monthly returns, worst month, holdings mix) and monthly dividend payers (10-K verified, with yield, coverage, leverage, book value change, dividend cuts and warning signs). Every company passes a soundness screen first: no warning filing in 12 months, assets greater than liabilities, 4+ audited years. Ranked screens, not recommendations; prices are the latest quarter-end 13F prices.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does add real behavioral context: the data is ranked screens rather than recommendations, prices are latest quarter-end 13F prices, and every company passes a stated soundness screen (no warning filing in 12 months, assets greater than liabilities, 4+ audited years). It still omits return format, pagination, and any auth/rate-limit behavior, so it is helpful but incomplete.

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

Conciseness3/5

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

The purpose is front-loaded, but the description is one dense block that crams three separate data categories (cash leaders, short-term places, monthly dividend payers) plus methodology into a single sentence-heavy paragraph. It is information-rich but poorly structured and longer than it needs to be for a no-argument retrieval tool.

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

Completeness4/5

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

For a zero-parameter tool with no output schema and no annotations, the description thoroughly covers the content categories and screening criteria an agent needs to judge relevance. Format, pagination, and volume of results are unstated, but the substantive return content is well described.

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?

The tool takes zero parameters, so there is no parameter semantics to explain; the baseline for a no-parameter tool is 4. The description does not need to compensate for any schema gap.

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

Purpose4/5

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

States a specific verb+resource (cash leaders) and enumerates exactly what the screen contains: top 20 US companies by cash returned to owners, operating cash/market value, and expected return, plus short-term funds and monthly dividend payers. It is clearly distinguishable from the parallel get_ai_leaders and get_energy_leaders siblings by content, though it never names them explicitly.

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

Usage Guidelines2/5

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

The description describes what is included but gives no when-to-use guidance, no exclusions, and no routing to alternatives. An agent cannot tell from this text when to prefer get_cash_leaders over get_ai_leaders or get_etf_health, so selection is left entirely to inference.

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

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