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Pixal3D-MCP

Pixal3D-MCP

by Lightfielder
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Python
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run_inference_sequence

Process an image sequence with Pixal3D inference to generate 3D assets. Auto-detects frame padding, pattern, and range for Maya, After Effects, or Fusion filenames.

Instructions

Run Pixal3D inference on an image sequence. Supports Maya, After Effects, and Fusion filename conventions. Auto-detects frame padding, sequence pattern, and frame range.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "input_path": "<input_path>"
    },
    "name": "run_inference_sequence"
  }
}

Input Schema

NameRequiredDescriptionDefault
seedNoRandom seed (default: 42)
frame_endNoEnding frame number (auto-detected if not specified)
frame_stepNoProcess every N frames (default: 1)
input_pathYesPath to an image in the sequence or sequence pattern (e.g., 'frames/image_0001.png')
auto_detectNoAuto-detect sequence pattern from filename (default: true)
frame_startNoStarting frame number (auto-detected if not specified)
max_num_tokensNoMemory limit (default: 4096)
output_templateNoOutput mesh template (e.g., 'output_####.glb', 'mesh.%04d.glb')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses the naming conventions supported and that padding/pattern/range are auto-detected, but this overlaps with the auto_detect, frame_start, and frame_end schema descriptions and says nothing about runtime cost, blocking behavior, or output artifacts.

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?

Three short sentences, front-loaded with the action and followed by supporting capability details. Nothing is padded, though the third sentence largely restates schema-level auto-detection behavior.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 8-parameter inference tool with no annotations and no output schema, the description is adequate but thin. It never indicates that this is likely a heavy/long-running GPU operation, whether results are written to files (implied only by output_template), or how the returned data should be interpreted.

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 every one of the 8 parameters is already documented in the schema with defaults and examples. The description adds only the generic claim of auto-detection and doesn't clarify interaction between auto_detect and explicit frame_start/frame_end, so baseline 3 applies.

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?

States a specific verb and resource ('Run Pixal3D inference on an image sequence') and the supported host conventions (Maya, After Effects, Fusion), which clearly separates it from generic siblings like run_inference. It does not, however, explicitly name run_inference or say how the sequence variant differs from the single-image variant.

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?

Usage is implied by the name and description: use this when your input is a frame sequence rather than a single image. There is no explicit 'use this instead of run_inference when...' routing, no prerequisites, and no note on when auto-detection should be turned off.

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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