mcp-ambee

weather_latest

Get the real-time, latest weather observation including temperature, humidity, wind, and UV index 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`).
`units`	No	Optional. you can pass any of these three imperial, metric, si. And default value is imperial.
`locale`	No	Optional. If set, adds local time to the record.

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?

With no annotations, the description carries the behavioral burden. It discloses that the API resolves free-text place names itself, warns against pre-geocoding, and says not to call another tool first. This is meaningful behavioral context beyond what the schema states.

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 usage rules, then an explicit exclusion. Every sentence earns its place, and the bullet-style layout is easy for an agent to parse.

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 output schema and the fully documented parameters, the description is complete enough for correct invocation. It covers what the tool returns, how to select a location form, and the key workflow trap to avoid. No critical calling information is missing.

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 on top by explaining the mutually exclusive lat/lng vs place relationship, providing place-name examples, and giving a heuristic for when to use coordinates. It does not elaborate on units or locale, but the schema already covers those clearly.

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 specific verb and resource: 'Get the real-time, latest weather observation including temperature, humidity, wind, and UV index for a location.' It clearly differentiates from the forecast and air-quality/pollen siblings through the 'latest weather observation' framing and its listed fields.

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?

It gives explicit instruction on how to choose between lat/lng and place, including a concrete 'do not pass both' rule and examples. It does not explicitly name alternatives like weather_forecast or air_quality_latest, but the 'latest observation' wording carries much of that context.

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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