Screen by Bullrun valuation model

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

Annotations cover read-only/destructive/open-world, but the description adds real context: the universe cutoff, what each row returns (valuation gap, model-typical vs current P/E, percentile, rank, data quality), and an explicit caveat that the gap is descriptive context rather than a return prediction. That interpretation constraint materially changes how an agent presents results.

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?

Front-loaded with the verb and universe, and every sentence carries new information (row contents, caveat, follow-ups). It is a dense paragraph that could be segmented for scanability, but there is no filler.

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?

With no output schema, the description compensates by naming the returned fields and the universe scope, and it closes the loop with follow-up tools so the agent knows this is a starting point. Nothing needed to invoke correctly 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 86%, so the schema already documents end, model, sector, country, coverage and exchange in detail. The description restates the model families and filter dimensions in prose but adds little syntax or semantics beyond the enum descriptions, so baseline 3 applies.

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?

States a specific verb (Rank) and resource (the whole scored stock universe, market cap >= $100M) plus the exact ranking basis (Bullrun ML valuation gap or forecast signal). This is unmistakably distinguishable from generic siblings like screen_stocks or get_valuation_model.

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?

Gives concrete example queries ('cheapest stocks vs fundamentals in Europe', 'best forecast scores in Japan') and prescribes follow-up tools (get_stock_metrics, get_company_risk, get_quality_moat_metrics). It never states when to prefer screen_stocks or get_valuation_model instead, so no explicit exclusion guidance.

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