mcp-ambee

air_quality_latest

Get the real-time, latest air quality reading and individual pollutant concentrations for a location.

You must provide EITHER:

lat and lng (if you already have or confidently know the coordinates), OR
place (a free-text place name, e.g. "Bengaluru", "Baker Street, London", "90210, US") — the API resolves this to a location itself, so do not try to geocode it yourself first, and do not call any other tool before this one.

Do not pass both lat/lng and place at once — pick one form.

Input Schema

TableJSON Schema

Name	Required	Description
`lat`	No	Latitude of the location (omit if using `place`).
`lng`	No	Longitude of the location (omit if using `place`).
`place`	No	Free-text place name like a city, street, or postcode with country (omit if using `lat`/`lng`).
`locale`	No	Optional. If set, adds local time to the record.
`aqiStandard`	No	Optional. The standard used to calculate the Air Quality Index. You can pass any of these five: EPA, IN, UK, CN, CA. And default value is EPA.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 7, 2026

TDQS

A4.5/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It usefully discloses that the API resolves free-text place names itself, that the agent should not attempt geocoding, and that lat/lng and place must not both be passed. This goes well beyond the schema. It stops short of describing failure behavior or limits, but the core behavioral traits are covered.

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 well-structured and front-loaded: purpose first, then parameter rules in a clear either/or layout. Every sentence earns its place, including the practical warning not to geocode or call other tools.

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 five optional parameters, the mutually exclusive input modes, and the presence of an output schema, the description is complete enough. It explains what the tool returns, how to specify the location, which parameters are optional, and the key behavioral constraints an agent needs to invoke it correctly.

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 real value by explaining the decision between coordinate forms ('if you already have or confidently know the coordinates'), clarifying that the API resolves place names itself, and warning against passing both forms. This goes beyond the schema's brief 'omit if using' notes.

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 opens with a clear verb and resource: 'Get the real-time, latest air quality reading and individual pollutant concentrations for a location.' This distinguishes it from siblings like air_quality_forecast, pollen_latest, and weather_latest by explicitly scoping it to current air quality data.

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 makes the intended usage context clear: it is for latest air quality at a location, with explicit either/or instructions for lat/lng versus place. It also tells the agent not to geocode or call other tools first. However, it does not explicitly name alternatives or state when to prefer a forecast or pollen tool over this one.

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

A4.6/5.0

Disambiguation5/5

Each tool has a unique combination of domain (air quality, pollen, weather) and temporal scope (forecast, latest), making selection unambiguous. No two tools overlap in purpose.

Naming Consistency5/5

All tool names follow a strict {domain}_{time_type} pattern, with every domain offering a _forecast and _latest variant. This creates a highly predictable and coherent naming scheme.

Tool Count5/5

With exactly 6 tools covering three environmental domains and two temporal modes, the count is well-balanced and each tool earns its place. The set is neither bloated nor thin.

Completeness5/5

The server provides both real-time and forecast data for all three core domains it targets (air quality, pollen, weather), offering complete coverage for its apparent purpose. No obvious missing operations that would cause agent failures.

Resources

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