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touchbridge

by GaNotchVFX
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Python
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data_chop

Read CHOP channel values from TouchDesigner, downsample to a max sample count, and choose output detail (full, summary, or minimal) in YAML or JSON.

Instructions

Read CHOP channel values (uniformly downsampled).

path (<class 'str'>): CHOP operator path.

max_samples (int | None): Max samples per channel (default 1024).

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
detailNo
max_samplesNo
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

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden and does so well: it discloses uniform downsampling, the default sample cap, summary/minimal output truncation behavior, and token-cheap YAML vs JSON. It stops short of describing exact output shape or error behavior, but the read-only character is clear.

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 definition is tightly structured: one purpose sentence followed by a compact parameter list. Every line adds information, and the default values and output options are front-loaded in a scannable format.

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?

Despite lacking an output schema, the description gives enough context to invoke the tool correctly: it names the resource, covers all parameters, and describes response detail levels and formats. It could go further by sketching the full response structure, but for a simple read tool the coverage is strong.

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%, yet the description fully compensates by explaining all four parameters: what path refers to, what max_samples caps, the exact detail modes, and the response_format options. It even clarifies effective defaults (1024, full, yaml) that the schema leaves as null.

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 one-line summary, 'Read CHOP channel values (uniformly downsampled)', names a specific verb and resource and adds a key scoping qualifier. It clearly distinguishes this tool from siblings like data_top, data_sop, and data_dat by targeting CHOP channel data.

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 makes the intended use clear: read CHOP channel values. It does not explicitly name alternatives or when-not-to-use conditions, but the domain-specific phrasing is enough for an agent to know when this tool applies versus related data_* 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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