koalcheck

score_ticker

★ COMPOSITE (Pro). One signed score (−100 bearish … +100 bullish) that blends the analysts who called this ticker — each vote WEIGHTED BY THEIR TRACK RECORD (the moat) — with SEC insider buying/selling and free retail (StockTwits) sentiment.

Shows a transparent per-component breakdown + coverage + confidence; absent components are renormalized away (not treated as neutral). FREE — reads stored analyst calls + free SEC/StockTwits (run analyst_views on your analysts first to fill the analyst leg). as_of (YYYY-MM-DD) bounds it point-in-time. audience: 'retail' (大白话) or 'pro' (default from EXPLAIN_MODE). Not advice.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	No
`ticker`	Yes
`audience`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries full burden. It discloses the score range (-100 to +100), weighting method (by track record), treatment of absent components (renormalized, not neutral), and that it's not advice. It does not mention auth needs or destructive actions, but as a read-only score, this is acceptable.

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?

The description is reasonably concise for the information conveyed, using bullet-like formatting. It front-loads the purpose and key characteristics. Minor redundancy (e.g., 'FREE' and 'not advice' could be integrated) but overall efficient.

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 presence of an output schema, the description does not need to detail return values. It covers all inputs, behavior, prerequisites, and caveats. The composite nature and component breakdown are clearly explained, making it self-sufficient for an AI agent to invoke correctly.

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 coverage is 0%, so description must add meaning. It specifies as_of format (YYYY-MM-DD) and that it bounds point-in-time, clarifies audience values ('retail' or 'pro') with default from EXPLAIN_MODE. Ticker is implied by context. This adds necessary detail beyond the schema.

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 produces a composite score blending analysts (weighted by track record), insider activity, and retail sentiment, with a per-component breakdown. It distinguishes itself from sibling tools like analyst_views (individual analyst leg) and stocktwits_symbol (raw sentiment) by being a composite.

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 provides usage guidance by stating a prerequisite: run analyst_views first to populate the analyst leg. It also clarifies the meaning of parameters like as_of (point-in-time) and audience (retail/pro). However, it lacks explicit when-to-use versus sibling tools, though the composite nature implies use when a consolidated signal is needed.

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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