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Glama

Smarter Weather

by io.github.smarterweather

Query dataset

query_dataset
Read-onlyIdempotent

Raw time series from a specific dataset for specific variables at a point. Power-user access to any gridded product (NBM, HRRR, RRFS, GFS, RTMA, MRMS, air quality, ...). Time modes: hours (next N hours, default 24), time_start+time_end (explicit ISO-8601 window), or latest=true (single most-recent value). reference_time pins a specific model run, and each returned series reports the run that served it (reference_time, or reference_times when a series mixes runs) — check it before comparing two runs, since a run older than about 48 hours may no longer be available. For blended forecasts use get_forecast instead. Examples: {"location": "Denver", "dataset_id": "rrfs_surface", "variables": ["temperature_2m"], "hours": 18} or {"lat": 41.4, "lon": -92.9, "dataset_id": "rtma_conus", "variables": ["temperature_2m"], "latest": true}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude in decimal degrees (-90 to 90). Most tools also accept a `location` place-name string instead of lat/lon.
lonNoLongitude in decimal degrees (-180 to 180). For continental US use negative values (west of the prime meridian).
hoursNoForecast/lookahead hours from now (1-264). Default 24 when no other time mode set.
latestNoReturn only the most recent value (analysis datasets like RTMA/MRMS).
locationNoFree-text place: city ("Denver"), city+state ("Portland, OR"), US ZIP ("50219"), or "lat,lon" ("39.74,-104.99"). Provide either this OR explicit lat+lon, not both.
time_endNoISO 8601 window end (with time_start).
variablesYesStandard variable names (e.g. temperature_2m, precipitation). Discover with describe_dataset.
dataset_idNoDataset to query. Default: the NBM dataset for the location domain (nbm_conus/nbm_alaska/nbm_hawaii). Discover options with list_datasets.
time_startNoISO 8601 window start (with time_end).
reference_timeNoPin a specific model run (ISO 8601). Default: latest run.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
seriesYes
locationYes

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedOutput schema / properties / series / items / properties / reference_time
      Added value: +{
+  "description": "Model run behind every value in this series.",
+  "type": "string"
+}
    • addedOutput schema / properties / series / items / properties / reference_times
      Added value: +{
+  "description": "Distinct runs, when this series mixes more than one.",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so no safety concerns need repeating. The description adds valuable behavioral context: run availability (a run older than about 48 hours may no longer be available), returned series reports reference_time or reference_times, and that results may mix runs. This goes beyond the annotations' safety profile.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: it opens with the core purpose, then covers time modes, run-pinning caveat, sibling routing, and examples. Every sentence earns its place. Slightly dense, but effective for a power-user tool.

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?

The description covers the key usage patterns (hours, window, latest), the reference_time caveat, and sibling differentiation. The output schema exists, so return format details are not the description's job. The only minor gap is that it doesn't explain what happens if no time mode is specified beyond hours defaulting to 24, but the schema covers hours default. Overall nearly complete for a tool of this complexity.

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 description coverage is 100%, so the schema already documents all 10 parameters. The description adds context by explaining time modes and reference_time semantics, but doesn't need to repeat parameter definitions. Baseline 3 is appropriate since the schema carries the heavy lifting and the description supplements with usage context.

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 states a specific verb and resource: query raw time series from a specific dataset for specific variables at a point, and explicitly names the power-user scope (NBM, HRRR, RRFS, GFS, RTMA, MRMS, air quality). It distinguishes itself from get_forecast by saying blended forecasts should use get_forecast instead.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says when to use this tool vs alternatives: 'For blended forecasts use get_forecast instead.' It also explains the three time modes (hours, time_start+time_end, latest=true) and reference_time behavior, which tells an agent exactly which parameters to set for a given scenario. The examples further illustrate valid usage.

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.

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