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intelligent_3d_processing

Automatically processes batches of 3D scenes, using adaptive multi-step planning to apply the right material, modeling, and export operations based on each scene's needs and your stated goal.

Instructions

Intelligent batch 3D scene processing via FastMCP 3.1 SEP-1577 multi-step sampling.

The LLM autonomously queries material, modeling, and IO capabilities to build a processing pipeline tailored to each scene's needs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scenesYesList of scene dicts (keys: name, objects, format, etc.)
max_stepsNoMaximum reasoning loops (default: 5)
processing_goalYesWhat to achieve (e.g. "optimize all scenes for real-time rendering")
processing_strategyNo"adaptive" | "parallel" | "sequential"adaptive
available_operationsYesOperations the orchestrator may use

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

Annotations are all false and provide no positive safety profile. The description mentions multi-step sampling and autonomous capability queries, but does not disclose side effects, whether scenes are modified, or how other tools may be invoked. The jargon 'SEP-1577' adds no behavioral clarity.

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?

The description is two sentences and reasonably compact. However, the first sentence includes unexplained jargon ('FastMCP 3.1 SEP-1577') that could be removed without losing meaning.

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

Completeness2/5

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

This is a complex autonomous tool, yet the description is sparse, lacking usage scenarios, expected side effects, and explanation of the multi-step orchestration. The output schema exists, but the description does not sufficiently cover the tool's operational context.

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%, and the schema already explains scenes, processing_goal, and available_operations. The description adds no parameter-specific meaning, so it merely meets the baseline.

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 identifies the tool as 'Intelligent batch 3D scene processing' and mentions autonomous pipeline building, which conveys the primary function. However, it does not explicitly differentiate from siblings like agentic_blender_workflow or blender_batch, so it falls short of fully distinguishing the tool.

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?

No guidance is provided about when to use this tool versus alternatives. The description explains what the tool does but gives no context on suitable use cases, prerequisites, or exclusions.

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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