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FlightFinder Aviation Safety Data

wildlife_strikes

Read-onlyIdempotent

FAA National Wildlife Strike Database (US-government public domain). With no arguments: global totals, the full yearly series since 1990, and the leading airports, species and flight phases. Pass airport OR species for that breakdown. Slugs are lowercase and hyphenated (e.g. "kden", "mourning-dove").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
airportNoAirport slug for a per-airport breakdown.
speciesNoSpecies slug for a per-species breakdown.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesglobal totals, or the breakdown for one airport or species

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds useful context beyond annotations: default no-argument behavior, the yearly series since 1990, the breakdown options, and slug format expectations. It does not contradict the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each earning its place: source/licensing, default mode, and filtered mode with slug format. The most important usage distinction (no args vs one arg) is front-loaded before parameter details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only tool with two optional, fully documented parameters and an output schema, the description is complete. It explains what happens with no arguments, what happens with each filter, and how to format slugs; nothing needed for a correct call is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already covers both parameters at 100%, but the description adds meaningful semantics: airport and species are alternatives ('OR'), they request 'that breakdown', and slugs are lowercase and hyphenated with concrete examples. This gives an agent everything needed to construct a valid call.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the FAA National Wildlife Strike Database as the resource and enumerates what it provides: global totals, yearly series, leading airports/species/flight phases, or per-airport/per-species breakdowns. It lacks an explicit verb like 'retrieves' or 'returns', but the resource and scope make its purpose unambiguous and distinct from accident-focused siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives clear usage modes: call with no arguments for global aggregates, or pass airport/species for a breakdown. However, it does not explicitly state when to prefer wildlife_strikes over sibling tools or provide exclusions, so an agent must infer tool selection from the resource name and content.

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

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TDQS

A4.1/5.0
Disambiguation5/5

Each tool maps to a distinct resource or dataset: accidents, narratives, sources, airport references, and the three FAA summary datasets. Even the get/search accident pair is clearly split by retrieve-by-ID versus filtered-search, so an agent should not struggle to pick the right tool.

Naming Consistency4/5

All names use snake_case and are readable, with noun-style dataset tools like wildlife_strikes and laser_incidents alongside verb-prefixed actions like search_accidents and list_sources. This is a minor deviation from a strict verb_noun convention, but it remains predictable.

Tool Count5/5

Nine tools is appropriate for a safety-data server that spans an accident corpus, narratives, source metadata, airport lookups, and multiple FAA datasets. Each tool earns its place and there is no obvious bloat or thinness.

Completeness4/5

The surface covers accident search/retrieval, narrative access, source metadata, airport resolution, and three FAA summary datasets. Notable gaps are the lack of occurrence-level retrieval for drone/laser/wildlife events and no explicit slug/autocomplete endpoint for aircraft families, but agents can usually work around these.

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