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render

Create a figure from a collected battery dataset and save it to a JSON or HTML file. Choose a film-style 2D density rendering or per-cell layout to visualize cell data for analysis.

Instructions

Draw a collection and write the figure to path (.json or .html).

kind="film" gives a 2-D density rendering. Note it is a kind, not a layout: on cellpy 2.1.2 layout="film" silently drew lines instead (fixed in 2.1.3). The returned trace_types is how you check what you actually got.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNo
pathYes
handleYes
layoutNoper_cell
spreadNo
directionNo

Schema Changelog

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

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well by documenting a concrete version-specific bug (cellpy 2.1.2 layout='film' drew lines) and providing a verification strategy via returned trace_types. It also makes the file-writing side effect explicit, though it doesn't mention overwriting behavior.

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, front-loaded, and every sentence earns its place. The main action is stated immediately, and the version warning is dense but necessary context.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given six parameters, no output schema, and no annotations, the description is too sparse for a full invocation. The agent knows the output path and the film gotcha, but lacks clear semantics for handle, spread, direction, and most layout values.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It explains path formats and clarifies kind versus layout for 'film', but the required handle parameter is never described, and spread/direction/layout general semantics are left undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific action ('Draw a collection') and output ('write the figure to path'), with file formats (.json/.html). It is not a tautology and gives readers a solid sense of what render does, though it doesn't explicitly differentiate it from siblings like preview_collection or export_collection.

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

Usage Guidelines2/5

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

The description does not say when to choose render over the sibling tools such as preview_collection or export_collection. It contains a useful note about kind='film' versus layout='film', but that is parameter-level guidance, not tool-selection guidance.

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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