decibel-shield

Get estimated noise levels for a city

get_city_noise

Read-onlyIdempotent

Answers "how loud is Tokyo", "noise level in Mumbai", "is Delhi louder than London". Estimated day/night noise ranges (dB), rank among 50 major world cities, dominant noise sources and a confidence label, with WHO guideline context (53 dB Lden / 45 dB Lnight). Estimates synthesized from published sources, not measurements. For US cities computed from federal data use get_us_city_noise_exposure; for measured Europe data use get_europe_city_noise.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`city`	Yes	City name, e.g. "Tokyo" or "São Paulo"

Output Schema

TableJSON Schema

Name	Required	Description
`city`	Yes
`rank`	Yes	Rank among the 50 cities (1 = loudest)
`country`	Yes
`page_url`	No	Detail page when available
`confidence`	Yes
`attribution`	No
`day_range_db`	Yes	Estimated daytime range, e.g. "60-72"
`night_range_db`	Yes
`dominant_noise_sources`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedJun 10, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "attribution": {
+      "type": "string"
+    },
+    "city": {
+      "type": "string"
+    },
+    "confidence": {
+      "enum": [
+        "high",
+        "med",
+        "low"
+      ],
+      "type": "string"
+    },
+    "country": {
+      "type": "string"
+    },
+    "day_range_db": {
+      "description": "Estimated daytime range, e.g. \"60-72\"",
+      "type": "string"
+    },
+    "dominant_noise_sources": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "night_range_db": {
+      "type": "string"
+    },
+    "page_url": {
+      "description": "Detail page when available",
+      "type": "string"
+    },
+    "rank": {
+      "description": "Rank among the 50 cities (1 = loudest)",
+      "type": "integer"
+    }
+  },
+  "required": [
+    "rank",
+    "city",
+    "country",
+    "day_range_db",
+    "night_range_db",
+    "confidence"
+  ],
+  "type": "object"
+}

First observedJun 10, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint and idempotentHint. The description adds that estimates are 'synthesized from published sources, not measurements', which is important context beyond annotations. No contradictions.

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?

Every sentence adds value. The description is front-loaded with examples, followed by technical details, and ends with sibling comparisons. No wasted words.

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 (dB ranges, rank, sources, WHO context) and the presence of an output schema, the description covers all key aspects: expected query types, data origin, and comparison with alternative tools.

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?

Input schema has full coverage (100%) with a city description. The description provides example city names but no additional semantic nuance beyond what the schema already conveys.

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 begins with specific example queries ('how loud is Tokyo', 'noise level in Mumbai') and clearly states the tool estimates city noise. It distinguishes itself from siblings get_us_city_noise_exposure and get_europe_city_noise.

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

Usage Guidelines5/5

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

Explicitly states when to use this tool (for estimated global city noise) and when to use alternatives (US cities from federal data, measured Europe data). This provides clear decision guidance for an AI agent.

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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