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plot_grid_spectra

Plot a local grid's spectra at a chosen point to view the output shape without writing a script. Set grid, point, and wavelength range to render the spectra.

Instructions

Plot a local grid's spectra at one grid point.

Renders the spectra a grid holds at a chosen point, so the shape of the grid's output can be seen without writing a script. Loading spectra reads the full arrays, so this is slower than inspect_local_grid, and importing Synthesizer creates its data directories on first use.

Args: grid_name: Grid name, with or without a file suffix. spectra_types: Spectra to draw, defaulting to every spectrum the grid provides, capped at six. point: Axis name to value, each in that axis's own units, for example {"ages": 1e7, "metallicities": 0.01}. Every axis must be given. Defaults to the centre of the grid. wavelength_range: [low, high] in the grid's wavelength units. Defaults to the range carrying essentially all the luminosity, because a grid's full axis spans many decades and drawing all of it hides the interesting part.

Returns: A mapping with ok, the written path, the grid_point indices used, the spectra_types drawn, the wavelength_range shown, and the axes values at that point. On failure ok is False and error explains why.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pointNo
grid_nameYes
spectra_typesNo
wavelength_rangeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Install Server

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations present, the description carries the full burden and does so well. It discloses that loading reads full arrays (performance cost), that importing Synthesizer creates data directories on first use, and explains default behaviors such as capping spectra types at six and defaulting the wavelength range to the interesting luminosity range.

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 front-loaded with the core purpose, then adds a performance/side-effect note, then systematically documents each parameter and return value. It is longer than average, but every sentence contributes necessary information for correct invocation.

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 plotting tool with four parameters and no annotations, the description is complete: it covers all arguments, defaults, units, side effects, and failure behavior. The return mapping is also described even though an output schema exists, giving the agent a clear picture of the tool's contract.

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?

Schema description coverage is 0%, so the description must fully explain parameters, and it does. Each argument gets meaningful semantics: grid_name suffix tolerance, spectra_types default and cap, point as axis-to-value mapping with units and an example, and wavelength_range as [low, high] with a sensible default.

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 opening sentence states a specific verb and resource: 'Plot a local grid's spectra at one grid point.' It distinguishes this from siblings like inspect_local_grid and plot_grid_lines by emphasizing rendering spectra at a single point, so an agent can tell what this tool does without opening the schema.

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 context: use this to see the shape of a grid's output without writing a script. It also names inspect_local_grid as a faster alternative, implying a tradeoff. It does not explicitly say 'do not use when X', but the context is strong enough to guide 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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