Get Bullrun valuation model

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

Annotations already cover read-only/no-destruction, so the bar is lower, and the description still adds real substance: nightly recompute cadence, the sign convention of the gap ('positive = trades below its typical multiple'), the two financial bases with a non-US coverage caveat, and the descriptive-not-advisory framing. It does not say what happens when a ticker has no score or partial fundamentals, which is the main remaining behavioral gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The most decision-relevant facts (what it is, scope, cadence) are front-loaded, followed by model mechanics and then use cases. It is dense and long-ish, but each block earns its place except the gap-formula detail, which is more than an agent needs to select the tool.

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

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

No output schema exists, and the description compensates by enumerating the returned quantities (gap, implied model-typical P/E, percentile, rank in universe, 1-year relative return rank) for both valuation and forecast. For a single-parameter read-only tool with full annotation coverage, nothing needed to call or interpret it is missing.

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

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

Schema coverage is 100% and the tickers parameter already carries a detailed description including suffix handling and search_stocks guidance, so the schema does the heavy lifting. The description only echoes the 1-10 cardinality and adds a note that annual financials cover most non-US companies, which marginally informs which listings resolve. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: proprietary ML valuation and forecast scores for 1-10 stocks, recomputed nightly, with the exact model (LightGBM P/E or price/sales) and gap formula spelled out. An agent knows precisely what it gets back. It stops short of naming the nearest sibling (screen_valuation_model, which presumably ranks a universe) or distinguishing itself from get_forward_estimates, so it is clear but not sibling-differentiating.

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

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

Explicit use cases are given in quoted form ('is X cheap or expensive', 'what's X's fair P/E', 'compare valuations of X, Y, Z') plus a negative boundary: 'Descriptive context, not a price target or recommendation.' That is clear context and one exclusion, but no alternative tool is named for the screening/ranking case or for forward-estimate needs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.