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Get climate reports and records

get_climate_records
Read-onlyIdempotent

NWS daily climate data: type=reports returns CLI daily climate reports (observed high/low/precip vs normals per station); type=records returns RER record event reports (record highs/lows/rainfall actually set). Filter by wfo (3-letter office, e.g. DMX), station, date (YYYY-MM-DD), start/end range, or hours lookback. Examples: {"type": "records", "hours": 48} or {"type": "reports", "wfo": "DMX", "date": "2026-07-04"}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoRange end date, YYYY-MM-DD.
wfoNoWFO office filter (e.g. DMX, OUN).
dateNoSingle date, YYYY-MM-DD.
typeYesreports = CLI daily climate reports; records = RER record events.
hoursNoLookback window in hours (1-168) when no date/range is given.
startNoRange start date, YYYY-MM-DD.
stationNoStation identifier filter (reports only).
record_typeNoRecord type filter (records only), e.g. HIGH, LOW, RAIN.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes
resultsYes

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already establish readOnlyHint, idempotentHint, openWorldHint, and non-destructive behavior. The description adds meaningful behavioral context by explaining what each type actually returns (observed vs normals for reports; actually set records for records) and by showing example filter combinations. It does not contradict the annotations.

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 compact and front-loaded: it starts with the core resource, immediately distinguishes the two type modes, then lists filters, and finishes with concrete JSON examples. Every sentence contributes value and the examples make invocation behavior unambiguous.

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 rich schema with full parameter documentation, strong annotations, and an output schema, the description is complete enough for an agent to call this tool correctly. It covers the key selection logic between reports and records, available filters, and representative example calls. Nothing critical is missing.

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?

The input schema already has 100% description coverage for all eight parameters, including formats, constraints, and type-specific applicability. The description reinforces wfo format and date format and gives examples, but it does not add substantial meaning beyond the schema. 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 that this tool returns NWS daily climate data, and explicitly breaks out the two modes: type=reports returns CLI daily climate reports (observed values vs normals) and type=records returns RER record event reports. This makes the resource and the distinction between report types clear, and it differentiates the tool from climate-normals-focused siblings.

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 clear usage context by listing allowed filters (wfo, station, date, start/end range, hours lookback) and provides two concrete examples showing valid payloads. It does not explicitly name alternatives or state when not to use this tool, but the scope is clear enough for an agent to select it appropriately.

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

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, and the detailed descriptions generally prevent misselection. A few near-overlapping pairs exist — get_current_conditions vs get_observations, and get_forecast already bundling current conditions, alerts, and outlooks — so some ambiguity remains.

Naming Consistency4/5

The overwhelming majority of tools follow a get_<object>_<modifier> pattern in snake_case, and the non-get tools still use an imperative verb_noun form. The mix of verbs (get, list, describe, find, query, search, reverse) is a minor inconsistency, but the overall pattern is predictable.

Tool Count2/5

At 32 tools, the surface is heavy and exceeds the 25+ threshold for a large tool set. The weather domain justifies much of the breadth, but several tools overlap in scope and could plausibly be consolidated, making the count feel higher than necessary.

Completeness5/5

The tool set comprehensively covers current conditions, forecasts, hourly data, climate, alerts, severe weather, air quality, tropical systems, upper-air soundings, maps, model data, geocoding, and platform status. There are no obvious dead-end workflows, and raw access via query_dataset fills most remaining gaps.