koalcheck

screen_stocks

★ SCREENER (Pro). Rank a universe of tickers by the composite score.

source: 'analysts' (tickers your followed analysts have called — ranked by who's been RIGHT) | 'trending' (StockTwits + WSB retail-hot tickers) | or pass an explicit universe=[...]. mode: 'bullish' (highest composite first — multi-signal confluence) | 'divergence' (crowd hyped but smart money — insiders + accurate analysts — isn't; a caution/ short-watch list). FREE data (no paid X). Analytics, not advice.

Input Schema

TableJSON Schema

Name	Required	Default
`mode`	No	bullish
`limit`	No
`source`	No	analysts
`audience`	No
`universe`	No
`min_coverage`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A3.9/5.0

Behavior2/5

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

No annotations provided, so description carries full burden. Discloses it's a screener (Pro) and FREE data, but lacks details on rate limits, data freshness, or what 'composite score' means. Behavioral traits like computational cost or update frequency are missing.

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

Conciseness4/5

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

Relatively concise, front-loaded with purpose, and packs useful info into a few lines. Uses special characters and line breaks that may reduce clarity, but overall efficient. Each sentence adds value.

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?

Tool has 6 parameters (0 required) and an output schema. Description covers key options (source, mode, universe) and purpose, making it largely complete. Missing parameter details are partially offset by schema, but min_coverage and audience lack context.

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%, but description explains source and mode in detail, and mentions universe as explicit array. However, limit, audience, and min_coverage are not described, leaving gaps. Compensates significantly but not completely.

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?

Description clearly states the purpose: 'Rank a universe of tickers by the composite score.' It specifies two sources (analysts, trending) and two modes (bullish, divergence), distinguishing it from sibling tools like trending_tickers or leaderboard.

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

Usage Guidelines4/5

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

Provides guidance on when to use each source and mode, e.g., 'mode: bullish' for multi-signal confluence, 'divergence' for caution/short-watch. Mentions FREE data and that it's analytics, not advice. Does not explicitly state when not to use, but implies context.

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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TDQS

A3.5/5.0

Disambiguation4/5

Most tools have clearly distinct purposes (e.g., analyst_views fetches views, analyst_debate compares them, analyst_track_record scores accuracy). Some overlap exists between sentiment tools (stocktwits_symbol, ticker_social_sentiment) but descriptions clarify boundaries. Overall, an agent can differentiate them.

Naming Consistency3/5

Naming is mostly lowercase with underscores, but conventions vary: some use prefixes (analyst_, direction_review_), some are single words (quote, leaderboard), and others are verb_noun (score_ticker, screen_stocks). This inconsistency makes patterns less predictable, though prefixes help group related tools.

Tool Count3/5

With 24 tools, the server is slightly above the ideal range of 3-15 for coherence. While each tool seems justified for the financial analysis domain, the volume could be overwhelming. Some tools (e.g., tweet_store_stats, direction_review_batch) are operator-only, reducing the surface for typical agents.

Completeness4/5

The tool set covers core workflows: fetching analyst views, tracking accuracy, SEC fundamentals, insider activity, material events, live quotes, social sentiment, and screening. Gaps like earnings calendar or portfolio management are minor given the focus on analyst-driven analysis. The operator tools for direction review add internal completeness.

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