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GroundTruth - Environmental Records

by ai.agentlookups
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Score a location's public-record layers

location_scores
Read-onlyIdempotent

Layer scores for a US location: school quality, natural hazards, groundwater contamination, Superfund proximity, air quality, highway noise, walkability, crime, and climate pleasantness (pleasant days/year). Scores are 0-1 with 1.0 = favorable; null = cannot score that layer here. The scores are model estimates drawn from public datasets, not safety ratings. no_coverage marks a layer that is null because this location lies outside that layer's data, and caveats. says why. When a layer's number has limits the score alone does not show (a proxy, partial source data), caveats. states them. Superfund proximity weighs each cleanup site by the nearer of the area EPA maps for it and the one point EPA lists for it; where EPA maps no area, a site that stretches out (a canal, a plume) can be closer than the score reflects. The scores are not a safety assessment, and an absence of adverse signals is not a clean bill of health. Coverage: Superfund/toxic-release proximity national; air quality, groundwater, and natural hazards a San Francisco Bay Area grid (lat 37.0 to 38.6, lon -122.8 to -121.4, which also takes in Sacramento), null elsewhere; highway noise Bay Area; walkability California; schools all 50 states and DC (2023-2025 results); climate CONUS (gridMET 4km, 2015-2024); Alaska, the Florida Keys, Hawaii, Puerto Rico and the US Virgin Islands (Daymet V4, 2011-2020); within 40 km of 3 NOAA weather stations in American Samoa, the Northern Mariana Islands and Guam (2016-2025); null elsewhere; crime police incident feeds inside the city limits of San Francisco; Oakland; Chicago; New York City; Los Angeles; Dallas; Washington, DC; and Kansas City, Missouri only. School scores draw on the Stanford Education Data Archive (SEDA), whose terms require naming it as the source: Reardon, S. F., Ho, A. D., Shear, B. R., Fahle, E. M., Kalogrides, D., Saliba, J. (2024). Stanford Education Data Archive (Version 5.0). [https://purl.stanford.edu/cs829jn7849] [https://doi.org/10.25740/cs829jn7849] (also in citations.school_quality). Input is either a street address (geocoded by the US Census geocoder) or lat/lon coordinates.

Input Schema

NameRequiredDescriptionDefault
latNoLatitude (US)
lonNoLongitude (US; negative except Guam, NMI and the western Aleutians)
addressNoUS street address incl. city/state (alternative to lat/lon)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior5/5

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

Beyond the read-only, idempotent annotations, the description discloses that scores are model estimates from public datasets, not safety ratings, and explains null handling via no_coverage and caveats.<layer>. It also details coverage limits, Superfund proximity weighting, and school-data sourcing, which are substantial behavioral disclosures.

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

Conciseness2/5

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

The description is front-loaded with the key purpose, but it is extremely long and includes dense coverage lists, full citations, and caveat implementation details. Much of this is relevant but could be condensed or moved to output documentation.

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?

Given the tool's complexity, the lack of an output schema, and the many coverage-limited layers, the description is sufficiently complete. It explains score ranges, null semantics, caveats, source limitations, and input options.

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

Parameters3/5

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

Schema coverage is 100%, so the lat, lon, and address parameters are already well documented. The description adds that addresses are geocoded by the US Census geocoder and that address is an alternative to coordinates, but it does not clarify precedence if both are supplied.

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 first sentence clearly states the resource and scope: layer scores for a US location across named domains such as schools, hazards, groundwater, and walkability. It is highly specific, but it does not explicitly differentiate itself from sibling tools like environment_near or drinking_water.

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?

Usage is implied by the detailed coverage notes and the statement that input is an address or lat/lon coordinates. However, the description never says when to choose this tool over broadband_availability, drinking_water, or environment_near, nor does it state any when-not conditions.

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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