mcp-ambee

weather_forecast

Get an hourly weather forecast for the next 48 hours 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", "New York") — 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: Optional. If set, adds local time to each hourly record.
`units`	No	Optional. you can pass any of these three imperial, metric, si.
`locale`	No

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

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the burden and it discloses useful behavioral traits: the API resolves place names itself, so the agent should not geocode beforehand, and the lat/lng vs place forms are mutually exclusive. It does not discuss rate limits, errors, or side effects, but for a read-only weather forecast with an output schema these omissions are minor.

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 front-loaded with the core purpose and then gives compact, clearly formatted input rules. The bullet list makes the mutually exclusive options easy to parse, and every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a forecast tool with an output schema and no required parameters, the description covers the essential calling context: output granularity, input alternatives, and the no-geocoding rule. The only notable incompleteness is the absence of any explanation for the locale parameter, whose schema entry is also blank.

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?

The description adds real meaning beyond the schema for lat/lng/place by explaining the exclusive-or relationship and the API's place resolution behavior. Schema coverage is 80%, so the schema already handles most parameter meaning, but the locale parameter lacks a schema description and is not explained in the tool description, which is the main gap.

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 an hourly weather forecast for the next 48 hours for a location.' The temporal scope (48-hour hourly forecast) and resource (weather forecast) distinguish it from siblings like weather_latest and the air_quality/pollen tools without needing to name them.

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?

Usage guidance is explicit for inputs: the agent must choose either lat/lng or place, must not pass both, and should not geocode or call another tool first when using place. It does not explicitly name sibling alternatives such as weather_latest, so it stops short of full alternative routing.

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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