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leaderboard

The honest track-record leaderboard — who has ACTUALLY been right (priced vs SPY).

Reads the daily honest board (21d hit-rate, Wilson 95% CI, bull/bear split, cross-regime flag, point-in-time vs SPY). view: • 'proven' — PROVEN tier (Wilson-CI lower bound > 0.5 + cross-regime); the trust core • 'fade' (反指) — reliably WRONG (Wilson-CI upper bound < 0.5) — a CONTRARIAN signal, not a buy list • 'cross_regime' — PROVEN across multiple market regimes (most robust) • 'all' — every tracked analyst at this horizon horizon: '1d' | '5d' | '21d' (default 21d = the canonical settled window). NOT investment advice; a track record is an after-the-fact measurement — past accuracy ≠ future.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
viewNoproven
limitNo
horizonNo21d

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description provides key behavioral context: it's a read-only measurement, includes a disclaimer about past accuracy, and explains that the 'fade' view is a contrarian signal. It doesn't mention authentication or rate limits, but these are less critical for a read-only tool.

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 well-structured with a clear summary followed by parameter details. Some redundancy and stylistic emphasis (all caps) add minor noise, but it remains efficient and informative.

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?

Given the tool has 3 parameters and an output schema, the description covers the key inputs and output contents (e.g., Wilson CI, regime flags). It lacks mention of pagination or error handling, but the core functionality is well-documented.

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?

Despite 0% schema coverage, the description fully explains the 'view' and 'horizon' parameters with their options and meanings. However, it does not describe the 'limit' parameter, leaving its purpose implicit from default value.

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's a leaderboard of honest track records, comparing against SPY. It specifies the verb 'reads' and the resource 'daily honest board', and the detailed parameter descriptions differentiate it from sibling tools like analyst_track_record.

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

Usage Guidelines3/5

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

The description explains the meaning of each view option but does not explicitly tell the agent when to use this tool versus alternatives like analyst_track_record or analyst_profile. Usage context is implied but not directly stated.

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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