Skip to main content
Glama

agentic_blender_workflow

Plan and execute multi-step Blender workflows autonomously. Break down a 3D goal into steps, probe Blender capabilities, and iterate 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
max_stepsNoMaximum LLM-tool reasoning loops (default: 5)
workflow_promptYesNatural language description of the 3D workflow goal
available_operationsNoOptional list of operation names to constrain the plan

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

A3.8/5.0
Behavior3/5

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

It discloses the looping behavior and the max_steps stopping condition, which is useful given annotations are all false. However, it does not mention potential side effects on the Blender scene, what 'capability probes' entail, or resource/permission implications, leaving the agent under-informed for a tool that executes arbitrary workflows.

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 compact at two sentences, with the main action front-loaded. The reference to 'FastMCP 3.1 SEP-1577 sampling' adds jargon, but the text remains focused and readable.

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?

With an output schema present, return-value explanation is not needed. Yet the tool is complex and has an available_operations parameter not mentioned in the prose; also absent are side-effect warnings, failure behavior, and differentiation from many similar Blender workflow siblings. The description is adequate but not fully complete.

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% and each parameter already has a meaningful description. The prose does not add further semantic value to workflow_prompt, max_steps, or available_operations, so a baseline of 3 is appropriate.

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 opens with a specific verb and resource: 'Execute autonomous multi-step Blender workflows'. It distinguishes this tool from siblings like 'blender_workflow' by emphasizing the agentic, looped, sampling-driven behavior.

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 clearly conveys the context for use: autonomous, multi-step 3D workflows where the client LLM plans and loops. It does not provide explicit when-not-to-use guidance or name alternatives, so it misses the top bar, but the intended use case is clear.

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

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