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

Annotations already declare readOnlyHint=true, so the read-only nature is known. The description adds valuable context beyond annotations: the update cadence ('Updates: monthly'), the optional history behavior with Gold tier requirements, and the output components. This gives the agent useful expectations about freshness and response shape.

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 description is three well-structured sentences: the first states the output and definition, the second lists use cases, and the third notes update frequency. It is dense but free of fluff. Slight over-packaging in the use-case list, but overall it earns its place.

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 enumerating the key return fields: score, trend, per-make turnover in top_drivers, price-band distribution, confidence, and methodology_version. It also covers the optional history mode and update cadence. It is sufficient for an agent to know what it will receive and when to invoke the tool, though it doesn't describe value formats in detail.

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?

The single parameter 'days' is fully documented in the input schema (range, meaning, tier dependency). The description does not add any additional meaning beyond what the schema already provides. With 100% schema coverage, the baseline of 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 opens with a specific verb+resource: 'Returns a 0-100 US used general-aviation aircraft market liquidity score' and defines the methodology (listings-to-registered-fleet turnover for major GA makes). It clearly distinguishes this from the many sibling tools by naming the exact market, makes, and metric, making the tool's purpose unambiguous.

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?

The description explicitly lists when to call: 'when the user asks about used aircraft market liquidity, GA resale demand, or which makes sell fastest, or when timing an aircraft purchase, sale, consignment intake, or collateral valuation.' This provides strong contextual guidance, though it stops short of naming alternative tools for related but distinct queries or stating when not to use this tool.

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