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touchbridge

by GaNotchVFX
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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data_sop

Retrieve point count, positions, and bounds for a TouchDesigner SOP path, with summary or full detail and YAML/JSON formats.

Instructions

SOP point count/positions and bounds.

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

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
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

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the transparency burden and does a solid job: it discloses truncation behavior (summary cuts long lists to 25 plus a count), minimal-mode behavior (top-level scalars only), and the default response format (YAML, token-cheap). It does not mention side effects or error behavior, but this is not critical for the apparent read-only retrieval role.

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 information-dense: a one-line purpose followed by equally concise parameter definitions. Every sentence adds value, and there is no fluff or redundancy.

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 no output schema, no annotations, and only one required parameter, the description is nearly sufficient: it names the returned data (point count/positions/bounds), defines all parameters, and explains output-shaping options. It could add path format details or error behavior, but for a simple 3-parameter query tool it is quite complete.

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

Parameters4/5

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

Schema description coverage is 0%, so the description compensates well: it clarifies path as an SOP operator path, enumerates all detail values (full, summary, minimal), and enumerates response_format values (yaml, json) with default behavior. This adds meaning beyond the raw schema, though the path semantics remain somewhat terse.

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 the resource ('SOP') and the data returned ('point count/positions and bounds'), which is specific and distinct from sibling data tools. It lacks an explicit verb such as 'get' or 'return', but the tool name and phrasing make the retrieval intent clear.

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 provides no guidance on when to choose data_sop over siblings like data_chop or data_top, nor any exclusions or alternative conditions. It only explains parameter options, not when the tool should be used.

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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