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Get sounding chart

get_sounding_chart
Read-onlyIdempotent

Render the nearest RAOB (radiosonde) sounding as a Skew-T log-P + hodograph chart image for visual analysis: temperature/dewpoint traces, wind barbs, height-banded hodograph, and a derived-indices table (CAPE/CIN, lifted index, PWAT, shear, LCL). Soundings launch at 00Z/12Z so data can be hours old. Use get_sounding for the raw profile numbers. Example: {"location": "Norman, OK"}.

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).
unitNoTemperature axis display unitfahrenheit
scaleNoRaster scale factor (2 = retina; higher = larger image payload)
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. First observed

TDQS

A4.2/5.0
Behavior4/5

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

The annotations already indicate this is a read-only, idempotent, non-destructive operation. The description adds useful behavioral context beyond annotations by warning that 'Soundings launch at 00Z/12Z so data can be hours old,' which is critical for interpreting the chart's freshness. It also discloses what the rendered chart contains, giving agents realistic expectations about the output.

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 dense and efficient: the first sentence defines the tool and output contents, the second provides a critical staleness caveat, and the third routes to the sibling tool. Every sentence contributes actionable information without repetition or filler.

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 what the tool returns, the data source, the staleness caveat, and the relationship to get_sounding. With no output schema present, the listing of chart elements (temperature/dewpoint traces, wind barbs, hodograph, indices table) gives the agent a solid mental model of the result. Minor details like image format or error behavior are absent but not critical for invoking the tool correctly.

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 every parameter in detail. The description adds only the concrete example '{"location": "Norman, OK"}', which demonstrates valid usage but does not materially enhance parameter understanding beyond the schema. A baseline of 3 is appropriate because the description does not need to compensate for missing schema documentation.

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 action and object: 'Render the nearest RAOB (radiosonde) sounding as a Skew-T log-P + hodograph chart image.' It enumerates the visual contents and explicitly separates itself from get_sounding by saying 'Use get_sounding for the raw profile numbers.' This makes the tool's purpose unmistakable and distinct among the sibling tools.

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 clearly frames the intended use case ('for visual analysis') and provides an explicit alternative ('Use get_sounding for the raw profile numbers'). It does not fully spell out when not to use this tool, but the visual-vs-raw distinction is enough to guide an agent's selection.

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.