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touchbridge

by GaNotchVFX
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Python
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par_info

Fetch metadata for specified parameters of a TouchDesigner operator. Supports filtering by names, detail levels, and YAML or JSON output.

Instructions

Metadata for named parameters (or all if names omitted).

path (<class 'str'>): Operator path.

names (list[str]): Parameter names; None = all.

detail (str | None): full (default) | summary (long lists cut to 25 + count) | minimal (top-level scalars only).

response_format (str | None): yaml (default, token-cheap) | json.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
namesNo
detailNo
response_formatNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.4.0
    • addedInput schema / properties / detail
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Detail"
+}
    • addedInput schema / properties / response_format
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Response Format"
+}
  2. First observedv0.2.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It reasonably does so by explaining defaults, summary truncation behavior, minimal detail behavior, and the token-cheap YAML option. It does not explicitly state that the operation is read-only, but 'metadata' strongly implies a non-mutating query.

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 and efficient: one sentence for purpose followed by tight parameter definitions. Every clause adds information, and the most important usage scoping ('or all if names omitted') is front-loaded.

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?

For a no-annotation, no-output-schema metadata tool, the description covers invocation parameters, defaults, and response-format choices well. It stops short of describing the exact metadata object fields, but that is not essential for selecting and correctly invoking the tool.

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?

Since the input schema has 0% description coverage, the description must fully explain the parameters. It does: path is the operator path, names is a list with None meaning all, detail enumerates full/summary/minimal with exact behavior, and response_format enumerates yaml/json with defaults. This is a strong compensation for the schema gap.

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 that the tool returns metadata for named parameters, and that omitting names returns all parameters. It does not explicitly contrast with value-focused siblings like par_get or par_get_all, so sibling differentiation relies on naming convention rather than an explicit statement.

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

Usage Guidelines3/5

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

The description gives useful internal usage guidance, such as 'names = all', detail modes, and response formats. However, it does not explain when to choose this tool over par_get, par_set, or par_pulse, and it provides no exclusions or alternative routing.

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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