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List TouchDesigner Experimental Builds

list_experimental_builds

Browse TouchDesigner experimental builds grouped by feature area. Filter by rendering, Python API, operators, UI, or networking to find builds matching your needs.

Instructions

Lists recent TouchDesigner experimental/beta build series grouped by feature area. Feature areas: rendering, Python API, operators, UI, networking. Each entry shows the series ID, build range, stability status, and headline features. Use get_experimental_build with a series_id for full details on any individual series.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
feature_areaNoFilter series by feature area. One of: 'rendering', 'Python API', 'operators', 'UI', 'networking'. Omit to list all series grouped by every feature area.
show_operatorsNoInclude the list of experimental operators for each series (default: true)
stability_statusNoFilter by stability status. One of: 'experimental' (active pre-release), 'graduated' (became a stable release). Omit to include all statuses.
show_feature_flagsNoInclude the feature flag table for each series (default: false)
show_breaking_changesNoInclude a brief summary of breaking changes per series (default: false)
Behavior3/5

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

No annotations are provided, so the description carries the behavioral burden. It describes the output structure but does not disclose whether the operation is read-only, authentication requirements, or any limitations like pagination or result size. For a list operation, the risk is low, but transparency could be improved.

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 concise (three sentences), front-loaded with the core purpose, and structured to list feature areas and entry contents. Every sentence adds value, with no extraneous details.

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?

Given 5 parameters, no output schema, and no annotations, the description adequately explains the tool's purpose, output contents, and provides a cross-reference to a related tool. It lacks details on pagination or exact response format, but is fairly complete for a list tool.

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 5 parameters well. The description adds minimal extra meaning, such as listing the default behavior for omitting feature_area. Baseline score of 3 is appropriate as the description does not significantly enhance parameter understanding beyond the schema.

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 clearly specifies the tool lists experimental/beta build series grouped by feature area, with explicit feature areas and entry contents. It distinguishes from the sibling tool get_experimental_build which retrieves details for a specific series.

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 states when to use this tool (to list recent experimental builds grouped by feature area) and directs users to get_experimental_build for full details on a specific series. However, it does not explicitly exclude cases or compare to other list tools.

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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