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

Annotations only give the generic readOnly=false/openWorld=true/destructive=false profile, so the description carries real weight: it discloses that the tool is machine-generated, the date boundary (2020) that switches between the contemporary and deep-history longitudinal models, and that richer output requires opting in via include. It does not cover auth, determinism, or latency, but the model-selection disclosure is genuinely useful and non-obvious.

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?

Four sentences, front-loaded with what the tool is, then input modes, then the date-driven model switch, then the optional expansions. No filler; every clause conveys an actionable fact.

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 return values need not be described, and the description covers input modes and model selection well. The main residual gap is guidance on choosing this tool over sibling valuation/cost tools, which is minor given the schema and annotations are otherwise rich.

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 already 100%, so the baseline would be 3, but the description adds meaning beyond the schema: the as_of_date value is not just a date but a model selector, and include is framed as an output-expansion switch. The tail-vs-spec either/or relationship between registration and make_model_id+aircraft_info is also made explicit.

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+resource ('machine-generated aircraft market value') with scope (single point in time, back to 1960) and even self-identifies as 'the flagship valuation tool'. It is clearly separable from siblings like create_valuation_report, estimate_cost_of_ownership, and impute_aircraft_specs.

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 clear input-mode guidance: 'Provide a tail number OR a full spec (make_model_id + aircraft_info)', and explains that as_of_date selects which model runs. It stops short of stating when to prefer this over create_valuation_report or estimate_cost_of_ownership, so it lacks explicit alternatives/exclusions.

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