API Acre

data.current-weather

Read-onlyIdempotent

Get one current weather snapshot for a city/locality name or exact coordinates. Returns the nearest NOAA/NWS station observation where available; otherwise returns the latest MET Norway model point, always labeled explicitly as observation or model with bounded SI fields and source provenance.

Input Schema

TableJSON Schema

Name	Required	Description
`city`	No	City or locality name, optionally followed by country and first-level region qualifiers, for example London, GB or Paris, Texas, US. Provide either city or both latitude and longitude.
`latitude`	No	Latitude in decimal degrees, rounded to four decimal places. Provide with longitude instead of city.
`longitude`	No	Longitude in decimal degrees, rounded to four decimal places. Provide with latitude instead of city.

Output Schema

TableJSON Schema

Name	Required	Description
`data`	Yes	Structured Current weather snapshot result
`meta`	Yes
`service`	Yes
`version`	Yes
`request_id`	Yes	Unique request identifier

TDQS

A4.5/5.0

Behavior5/5

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

Annotations already declare read-only and idempotent behavior, and the description adds substantial context: fallback from NOAA/NWS observation to MET Norway model, explicit labeling of observation vs model, bounded SI fields, and source provenance. This goes well beyond the safety hints.

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?

Two compact sentences front-load the core purpose and then supply essential data-source details. No filler, no repetition of schema content, every sentence earns its place.

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?

With an output schema present, the description does not need to detail return fields. It covers source selection, fallback behavior, labeling, unit constraints, and provenance—sufficient for an agent to invoke correctly across the oneOf parameter patterns.

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?

Schema coverage is 100% and each parameter already has rich descriptions including examples and constraints. The tool description repeats the city-or-coordinates alternative but adds no new parameter-level meaning beyond what the schema provides. Baseline 3 is appropriate.

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 clearly states the tool gets one current weather snapshot for a city/locality or coordinates, immediately distinguishing it from sibling data.us-weather-forecast (forecast vs current) and geocoding tools. The verb 'Get' is specific and the resource is precisely scoped with data-source context.

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 provides clear context for when to use the tool (current weather vs forecast) but does not explicitly name alternatives or state when not to use it. The city-or-coordinates disambiguation is implied through the schema rather than directly spelled out in usage terms.

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

A3.8/5.0

Disambiguation4/5

Tools are grouped into clear domain prefixes (crypto, data, developer, document, research, web) and each tool name describes a specific function; however, a few umbrella tools like web.full-audit and data.contract overlap with their more targeted counterparts, creating minor ambiguity.

Naming Consistency5/5

All tool names follow a consistent pattern: a domain prefix, a dot, and a hyphenated lowercase compound name (e.g., crypto.base-block-inspect, web.seo-audit). This makes naming predictable and easy to scan.

Tool Count1/5

At 63 tools, the surface area is very large and exceeds the 50+ threshold for extreme mismatch. While the tools are organized into six domains, the sheer number makes it difficult for an agent to select efficiently, and some tools are bundled combinations of others.

Completeness5/5

Each domain offers a thorough set of operations: crypto covers address, account, block, contract, events, gas, and transaction inspection; data covers cleaning, conversion, schema, and validation; developer covers code review, dependency/license audits, and test generation; research covers SEC, OFAC, GLEIF, and USAspending; web covers extraction, SEO, security, and performance. No obvious dead ends exist for the read-only/inspection purpose.

Resources

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