koalcheck

analyst_views

★ CORE. Fetch the recent views of specific X analysts/KOLs by handle.

The user names the analysts they follow (e.g. ["DeItaone", "unusual_whales"]). Optionally focus on one ticker. Returns, per analyst: overall stance, which tickers they're talking about, and their recent points.

Present this to the user as a SUMMARY in your own words ("最近 @X 看好…") — do NOT reproduce the original tweets verbatim. Attribute each view to its handle.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ticker`	No
`handles`	Yes
`limit_per_analyst`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A3.8/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. It describes return format (overall stance, tickers, points) and instructs to present as summary, not verbatim. Lacks mention of auth or rate limits.

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 description is moderately concise but includes extended instructions for presentation. Front-loaded with 'CORE' but could be trimmed without losing meaning.

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

Completeness3/5

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

Given 3 parameters, no annotations, but output schema present, the description covers main behavior. Lacks detail on edge cases or full parameter semantics.

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

Parameters2/5

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

Schema coverage is 0%, and description only mentions two parameters (handles and ticker) but omits limit_per_analyst. Fails to fully compensate for missing schema descriptions.

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 the tool fetches recent views of specific analysts by handle, with a specific verb and resource. It distinguishes from sibling tools like analyst_debate and analyst_profile.

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 explicit usage context with examples and optional ticker focus. It gives guidance on how to present results but lacks explicit when-not-to-use or alternative tools.

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.

Resources

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