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collect_site_data

Collect comprehensive environmental data from over 80 US federal sources for a site. Accepts address, coordinates, or GeoJSON geometry. Returns a jobId; poll with get_results for flood zones, soils, wetlands, contamination, and more.

Instructions

Collect comprehensive environmental data from ALL 80+ federal data sources for a site. Accepts an address, lat/lng coordinates, or a GeoJSON geometry (Point or Polygon). Returns a jobId — poll with get_results until complete (60-120 seconds).

Data returned covers: flood zones, wetlands, soils, geology, contamination sites, water quality, seismic risk, rainfall, infrastructure, ecology, energy, demographics, and much more.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
addressNoUS street address (e.g., "123 Main St, Houston TX"). If provided, the site is geocoded automatically. Use this OR lat/lng OR geometry.
latNoLatitude of the site (e.g., 34.8441). Use with lng.
lngNoLongitude of the site (e.g., -82.4010). Use with lat.
geometryNoGeoJSON geometry (Point or Polygon). For advanced use — most users should use address or lat/lng instead.
bufferAcresNoSite area in acres when using a point location. Creates a circular buffer. Default: 1 acre. Range: 0.1–640.
searchRadiusMilesNoHow far to search for nearby features (contamination, infrastructure, etc.). Default: 3 miles. Range: 0.5–10.
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses async behavior, polling requirement, time estimate, and broad data coverage. It does not mention rate limits or availability constraints.

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?

The description is concise with two front-loaded paragraphs. Every sentence adds value with no redundancy or waste.

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

Completeness4/5

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

Given no output schema, the description covers the return format (jobId), polling mechanism, and data categories. It is fairly complete but could mention error handling or limits.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for each parameter. The description adds value by summarizing the OR condition among address/lat-lng/geometry and providing default ranges for bufferAcres and searchRadiusMiles.

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

Purpose5/5

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

The description clearly states the tool collects comprehensive environmental data from 80+ federal sources, accepting address, lat/lng, or geometry. It uses a specific verb-resource combo and distinguishes from siblings like get_results.

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

Usage Guidelines4/5

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

The description explains how to use the tool (provide location input, receive jobId, poll with get_results) and gives typical completion time. It does not explicitly state when not to use it, but the context is clear.

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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