mcp-ambee

pollen_latest

Get the real-time, latest pollen count and risk levels for tree, weed, and grass pollen 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.
`species_risk`	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 provided, the description carries the burden of behavioral disclosure. It reveals that the API resolves place names itself, warns against geocoding, and enforces a call-order constraint. It does not discuss failure modes or ambiguous places, but it covers the most important behavioral traits.

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: purpose first, then location requirements, then the exclusions. Every sentence provides actionable guidance, and the line breaks make the either/or constraint easy to parse.

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 tool with an output schema, the description sufficiently explains how to select and pass location. The only notable gap is the undocumented species_risk parameter, which the agent would have to infer from the name. Overall, the essential calling context is present.

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 80%, and the description adds meaningful disambiguation beyond the schema: the either/or relationship between lat/lng and place, and the instruction not to pass both. The species_risk parameter has no schema description and is not clarified in the tool description, which is a minor 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 the real-time, latest pollen count and risk levels for tree, weed, and grass pollen for a location.' This clearly distinguishes the tool from its pollen_forecast sibling by emphasizing it returns current/latest data rather than a forecast.

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 gives explicit location-parameter prerequisites: either lat/lng or a free-text place, not both, and instructs not to geocode or call another tool first. It does not explicitly contrast with pollen_forecast or weather/air-quality siblings, but the 'real-time, latest' framing makes the primary use case clear.

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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