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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description does add useful non-annotation context: ranking by quality score, tier-based caps (Free top 3, Pro up to 20), that the result is the complete answer, and a disclaimer that this is a valuation, not advice. Behavior is only partially disclosed - rate-limit behavior is left to the schema's request_id/analysis_session_id rather than explained in prose.

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 purpose is front-loaded well, but the field enumeration is bulky and the deep_link explanation is largely repeated ('deep_link (full analysis page on fairvalue-calculator.com...)' followed later by 'deep_link opens the full analysis page with the fair value history chart'). That redundancy costs efficiency.

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 carries the burden of explaining return values and it does so thoroughly: valuation_status thresholds, upside_band semantics, quality_score range, evidence_level, and deep_link. Tier limits and the next-step pointer to deep_link make it actionable; only error/failure behavior is unexplained.

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%, so the schema already documents all 7 parameters including defaults for min_quality and min_upside_pct. The description adds semantic depth for returned fields rather than parameters, and only indirectly ties the tier caps to 'limit'. Baseline 3 is correct when the schema does the heavy lifting.

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 ('Find stocks that currently match valuation criteria') and adds a scope qualifier ('from Fair Value Calculator data only') that separates it from unrelated screening. It does not explicitly name a sibling tool to differentiate from (e.g. get_stock_verdict or suggest_watchlist_stocks), so it falls short of a 5.

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?

Concrete trigger examples are given ('which stocks are undervalued right now', 'undervalued European tech stocks'), which tells the agent when to reach for this tool. No when-not conditions or named alternatives are provided, so no exclusions to guide the agent away.

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