Skip to main content
Glama

agentic_blender_workflow

Plan and execute complex 3D workflows in Blender by describing the goal in natural language. The AI autonomously breaks down the task, calls Blender operations, and iterates until completion.

Instructions

Execute autonomous multi-step Blender workflows via FastMCP 3.1 SEP-1577 sampling.

The client LLM plans and executes a 3D workflow step-by-step, autonomously calling Blender capability probes to inform each decision, looping until the goal is achieved or max_steps is exhausted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflow_promptYesNatural language description of the 3D workflow goal
available_operationsNoOptional list of operation names to constrain the plan
max_stepsNoMaximum LLM-tool reasoning loops (default: 5)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description covers key behavioral traits (looping, probing, autonomous planning) but does not disclose potential side effects, required permissions, or failure modes, leaving moderate 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?

Two concise sentences: first specifies purpose and technology, second explains behavior. No redundancy, every sentence adds value.

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?

The description covers high-level behavior, loop count, and constraints. An output schema exists for return details, so return values are presumably documented. Missing details about prerequisites (e.g., active scene) are minor.

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 coverage is 100%, so baseline is 3. The description adds overall context but does not enhance individual parameter meanings beyond what the schema already provides.

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 the tool executes autonomous multi-step Blender workflows via FastMCP sampling, differentiating it from sibling tools like blender_workflow and other specific operation tools by emphasizing autonomous and multi-step capabilities.

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 indicates when to use the tool (autonomous multi-step workflows) and explains the looping mechanism up to max_steps, but does not explicitly exclude alternative tools or mention when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sandraschi/blender-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server