koalcheck

quote

Live price + volume + turnover (换手率) + market cap + basic valuation for a ticker.

Returns last price & % change, day open/high/low, volume + 10-day avg volume, turnover_pct (换手率 = volume ÷ shares outstanding), market cap, 52-week range, and best-effort trailing/forward P/E + P/S + sector. Intraday values are delayed ~15m. FREE (yfinance) — does NOT consume the daily live-fetch quota. Pair with score_ticker / fundamentals / analyst_track_record for the full picture.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ticker`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.3/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It explicitly states that intraday values are delayed ~15m and that it uses yfinance (FREE, no quota consumption), which are critical behavioral traits. It could mention rate limits or data source limitations, but overall it is transparent.

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 concise, front-loaded with the main purpose, and uses a clean bullet-like structure to list return fields. Every sentence adds value, with no fluff or redundancy.

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?

Given the tool's simplicity and the presence of an output schema, the description is complete. It covers the data returned, data freshness, cost, and related tools, leaving no significant gaps for the agent.

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

Parameters3/5

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 does not explicitly describe the 'ticker' parameter's format or accepted values, but the context (e.g., 'for a ticker') and the list of returned fields imply it is a stock ticker symbol. This adds some meaning but could be more explicit.

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 clearly states it provides 'Live price + volume + turnover + market cap + basic valuation for a ticker', specifying the exact resource and action. It distinguishes from siblings by suggesting pairing with score_ticker, fundamentals, etc., showing it is a standalone quick quote tool.

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?

The description indicates when to use (live price data) and notes that it is FREE and does not consume daily quote quota, implying it's safe and always available. It advises pairing with other tools for a fuller picture, though it does not explicitly state when not to use it.

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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