decibel-shield

Modeled transportation noise at a US address

get_address_noise

Read-onlyIdempotent

Answers "how loud is my neighborhood", "noise map by address", "how noisy is this address", "noise level in ZIP 78701". Modeled transportation noise (road + rail + aircraft, 24-hour average LAeq dBA) at a US street address, a 5-digit ZIP (sampled at the ZIP Code area center, approximate) or lat/lng, sampled block-by-block from the federal BTS 2020 National Transportation Noise Map. Returns a modeled dBA band, NOT a measurement; "below 45 dBA" means no modeled freeway/rail/flight path nearby (model floor, not silence). Continental US only.

Input Schema

TableJSON Schema

Name	Required	Description
`lat`	No	Latitude (use with lng instead of an address)
`lng`	No	Longitude (use with lat)
`zip`	No	5-digit US ZIP Code; sampled at the center of the ZIP Code area (Census 2023 ZCTA), so approximate
`address`	No	US street address, e.g. "1600 Pennsylvania Ave NW, Washington DC"

Output Schema

TableJSON Schema

Name	Required	Description
`lat`	No
`lng`	No
`band`	Yes	Modeled dBA band, e.g. "60–70 dBA", or "below 45 dBA"
`note`	No
`query`	No
`db_low`	No
`db_high`	No
`modeled`	Yes
`attribution`	No
`matched_address`	No	Geocoder’s normalized address (when an address was given)
`nearby_max_band`	No	Loudest modeled band within ~90 m, if higher than the point

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 19, 2026

addedInput schema / properties / zip

Added value: +{
+  "description": "5-digit US ZIP Code; sampled at the center of the ZIP Code area (Census 2023 ZCTA), so approximate",
+  "type": "string"
+}

AddedJul 11, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses important behavioral traits: results are modeled bands, not measurements; ZIP sampling is approximate at the ZCTA center; 'below 45 dBA' is a model floor, not silence; and coverage is continental US only. This significantly helps the agent interpret outputs correctly.

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 tightly written, front-loaded with user-intent examples, and every sentence adds value: purpose, data source, output semantics, and limitations. No filler or repetition of schema 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?

The description is complete for a read-only lookup tool: it specifies inputs, data source, output band, what the floor value means, and geographic coverage. The output schema covers return structure, so the description does not need to repeat it.

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%, so the baseline is 3. The description adds meaningful semantics by explaining how each input is sampled (e.g., ZIP center approximation, lat/lng as an alternative to address) and clarifying the model's resolution. This goes beyond the schema's basic parameter descriptions.

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 states exactly what the tool computes: modeled transportation noise (road, rail, aircraft) as a dBA band at a US address, ZIP, or lat/lng. It includes natural-language query examples and clearly distinguishes itself from city-level siblings by emphasizing address/point granularity.

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 clearly communicates valid input types (address, ZIP, lat/lng) and a key limitation (continental US only, modeled not measured). However, it does not explicitly compare itself to siblings like get_city_noise or get_neighborhood_noise, leaving the when-to-use-vs-alternatives decision mostly to inference.

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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