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touchbridge

by GaNotchVFX
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Python
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data_dat

Read text or table DAT content from a TouchDesigner operator by path. Control row range, detail level, and YAML/JSON output for inspection.

Instructions

Read text or table DAT content.

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

row_start (int | None): First table row (default 0).

row_end (int | None): Last table row, exclusive.

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
row_endNo
row_startNo
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
Behavior3/5

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

With no annotations provided, the description carries the full burden. It states 'Read' indicating a read-only operation, and details response_format and detail options. However, it does not disclose error behavior, what happens for invalid paths, or the exact structure of the returned content. This is acceptable for a simple read tool but leaves some behavioral gaps.

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 a compact list of parameters with clear formatting, no redundant words, and the purpose statement is front-loaded. Each parameter is explained in a single line, making it highly scannable and efficient.

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 the tool's simplicity (read operation, 5 params with 1 required), the description covers parameter semantics and output formats. However, it does not explain the return structure in detail (e.g., what 'full' vs 'summary' actually returns) or error conditions. With no output schema, these gaps are minor but noticeable.

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?

The description provides inline documentation for every parameter, including defaults and allowed values (e.g., detail: full/summary/minimal, response_format: yaml/json). This adds substantial meaning beyond the schema, which has no descriptions. It fully compensates for the 0% schema coverage.

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 states 'Read text or table DAT content', specifying the verb 'read' and the resource 'DAT content'. It distinguishes from siblings like data_chop, data_top, data_sop, and data_dat_write, which target different operator types or write operations. The purpose is unambiguous.

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 implies usage for reading DAT operators but does not explicitly contrast with alternatives like data_chop or data_top. No when-to-use or when-not-to-use guidance is provided beyond the resource type implied by the name. The agent must infer from the tool name which sibling to select.

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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