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Smarter Weather

by io.github.smarterweather

Get map snapshot

get_map_snapshot
Read-onlyIdempotent

Render a weather map image for visual analysis. Simple form: pass product (a viz-catalog product_id like "mrms_qpe_01h_pass2_conus", "goes_truecolor_conus", "spc_day1_categorical", "hrrr_precip_hybrid_derived_conus" (future radar), "hrrr_subhourly_conus" (15-min Future Radar), "mrms_radar_nowcast_conus", "rtma_conus", "nbm_daily_temps", or "nexrad_l3:{SITE}:{PRODUCT}" for single-site radar, e.g. "nexrad_l3:TLX:N0B") plus a location and zoom (5=regional, 8=metro, 10=city). Composed form: pass scene -- a declarative scene document layering basemap + multiple weather products + active alerts + storm features + inline GeoJSON in one image (layers draw bottom-to-top, under basemap labels). Example scene: {"scene":"1.0","view":{"center":{"lat":43.8,"lon":-91.2},"zoom":8},"layers":[{"type":"weather","product":"goes_truecolor_conus"},{"type":"weather","product":"nexrad_l3:ARX:N0B"},{"type":"alerts","filter":{"events":["Tornado Warning"]},"onError":"skip"}]}. Alert filters (all optional, AND-combined): ids (specific alerts), events, severities, minSeverity (Extreme>Severe>Moderate>Minor>Unknown). Single-site radar keys: the address is nexrad_l3:{SITE}:{KEY} where KEY is N{tilt}{measurement} and tilt 0 is the 0.5 degree sweep -- N0B reflectivity (dBZ, where and how heavy), N0G base velocity (knots toward/away from the radar), N0S storm-relative velocity (storm motion removed, so a couplet is rotation rather than translation -- prefer it for rotation questions), N0C correlation coefficient (0-1, debris and hail), N0X differential reflectivity (dB). Legacy codes (N0V, N0R, N0Q) are accepted as aliases. Not every site produces every key; when a render reports which keys a site has, retry with one of those. Optional time (unix seconds): closest frame. Forecast (HRRR/nowcast/NBM) honors future times; analysis (MRMS/NEXRAD/RTMA/GOES) clamps to latest past. Pass time for future-radar asks — do not claim that capability is missing. Product ids must be real viz-catalog entries -- shorthand like "radar" or "reflectivity" is not one. Omit product for the default hybrid precip still. For Alaska and Hawaii prefer a local site or mrms_precip_hybrid_derived_alaska over CONUS mosaics, which do not cover them. Returns the rendered image plus per-layer resolved valid times.

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).
timeNoUnix seconds; closest frame (default: latest). Forecasts honor future times.
zoomNoMap zoom (simple form)
sceneNoFull scene document (composed form). When set, product/location/zoom are ignored.
widthNo
heightNo
opacityNoWeather layer opacity
productNoviz-catalog product_id or nexrad_l3:{SITE}:{KEY} (simple form)
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.

Schema Changelog

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

  1. Changed2 schema fields changed
    • changedInput schema / properties / height / default
      Previous value: -560New value: +440
    • changedInput schema / properties / width / default
      Previous value: -900New value: +720
  2. Changed1 schema field changed
    • changedInput schema / properties / time / description
      Previous value: -"Unix seconds; frame closest to this time (default: latest)"New value: +"Unix seconds; closest frame (default: latest). Forecasts honor future times."
  3. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description adds substantial behavior beyond that: forecast products honor future times while analysis products clamp to the latest past, the return includes per-layer resolved valid times, layers draw bottom-to-top under basemap labels, and NEXRAD site-key availability may require retrying. No contradiction with 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 long, but every sentence earns its place given 10 parameters and no output schema. It is front-loaded with the purpose and the simplest form, then moves to composed scenes, then edge cases and region-specific guidance. The structure makes the dense detail navigable.

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?

For a complex 10-parameter tool with nested scene objects and no output schema, the description is exceptionally complete: it documents the return value ('rendered image plus per-layer resolved valid times'), parameter interactions, product-id validity requirements, time semantics, and regional caveats. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Even though schema coverage is 80%, the description greatly expands meaning: it gives concrete product_id examples, explains the entire NEXRAD key format (tilt, measurement, and legacy aliases), defines alert filter fields and AND-combination semantics, provides a full scene example, and clarifies interactions like `scene` overriding product/location/zoom. The schema descriptions alone would not enable correct invocation nearly as well.

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-resource pair: 'Render a weather map image for visual analysis.' It clearly distinguishes the simple form (single `product` plus location and zoom) from the composed form (`scene` document), which separates it from all sibling data/forecast tools.

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 gives explicit when-to guidance: use the composed scene form for layering multiple products/alerts, pass `time` for future-radar asks, prefer local sites or Alaska-specific products over CONUS mosaics, and omit `product` for the default hybrid precip. It even warns against claiming future-radar capability is missing. While it doesn't name sibling tools, the map-rendering purpose is unambiguous among the listed siblings.

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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