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

Annotations already declare this a safe, idempotent, non-destructive read, so the safety profile is covered. The description adds genuinely useful method context (Qlib-inspired, IC analysis over 36 metrics) but says nothing about coverage limits, freshness, latency, or cost for an analytical scoring tool.

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?

Three sentences, no filler, and the core deliverable (composite alpha score across 1M/3M/12M horizons) is front-loaded. It is somewhat dense in enumeration but every clause carries information.

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?

An output schema exists, so the enumerated return fields are redundant but harmless; scope (NZX), method, and inputs are all stated. The remaining gap is comparative context versus the many closely named factor/alpha siblings, which matters given this crowded tool family.

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?

Single ticker parameter with 100% schema description coverage, including examples ('AIR', 'FPH', 'MEL'), so the schema fully carries semantics. The description adds only the NZX scope constraint, which is baseline-level value for a 1-param tool.

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 and resource ('Get Qlib-inspired alpha factor scores') and names the scope (NZX company) plus what the score is built from (36 fundamental/technical/governance metrics). However, it offers no differentiation from close siblings such as get_alpha_score, get_factor_model, get_factor_grades, or get_factor_validation, so an agent cannot tell which of these to pick from the description alone.

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?

It lists intended use cases ('quantitative analysis, factor exposure, alpha signals, or stock scoring'), which is clear enough context. But it names no alternatives and gives no when-not guidance, and 'stock scoring' overlaps heavily with sibling tools like get_alpha_score and screen_stocks, so routing remains ambiguous.

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