koalcheck

analyst_track_record

★ MOAT (Pro). How ACCURATE has this analyst been? Scores their past calls against what the stock actually did vs the market (SPY).

Resolves the @handle to the analyst's permanent account id (rename-proof), extracts scorable calls from stored tweets, evaluates each against historical prices at 1/5/21-day horizons (benchmark-adjusted abnormal return, point-in- time), and returns a scorecard: hit-rate + average abnormal return per horizon, how many posts were actual calls vs just news, and a sample-size caveat.

This is performance ANALYTICS (was the call right), NOT investment advice. Note: needs stored tweet history for the analyst; call analyst_views first to populate, and matured time windows to score (recent calls show as pending).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`handle`	Yes
`refresh`	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 name resolution, evaluation horizons, output components, and caveats. Lacks explicit read-only statement but context implies no destructive effects.

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?

Description is well-structured with star rating, question, block, prerequisites, and notes. Every sentence adds value, though could be slightly more concise.

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 complexity (multiple horizons, benchmark adjustment, sample-size caveat) and that an output schema exists, the description is thorough. Includes pending call handling and disclaimer.

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 coverage is 0%, so description must compensate. The handle parameter is well explained ('Resolves the @handle'), but the refresh parameter is not mentioned at all. Partial compensation.

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 opens with a clear question 'How ACCURATE has this analyst been?' and specifies the verb 'scores their past calls'. It distinguishes the tool from siblings like analyst_views (which populates data) and analyst_recent_calls (which lists calls).

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

Usage Guidelines5/5

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

Explicitly states prerequisites: 'needs stored tweet history; call analyst_views first', and explains when results are pending. This tells the agent when to use the tool and what to do beforehand.

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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