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Blender MCP Ultra

Blender MCP Ultra

by 3bdull4h2008
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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execute_sequence

Execute a sequence of Blender commands in one call, chaining operations for complex automation. Streamline multi-step workflows by running commands in order on the main thread.

Instructions

Execute a sequence of Blender commands in one call.

Each command: {"type": "create_object", "params": {...}} Commands are executed in order on the Blender main thread. This is the most powerful batch tool — it chains any operations.

Args: commands: List of command dicts with type and params.

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": {
      "commands": "<commands>"
    },
    "name": "execute_sequence"
  }
}

Input Schema

NameRequiredDescriptionDefault
commandsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.0.0

TDQS

B3.3/5.0
Behavior3/5

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

It discloses that commands run in order on the Blender main thread and gives the command dict format, which is useful. However, with no annotations, it omits critical behavior such as whether the sequence stops on error, atomicity, or what happens to partially executed commands.

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, front-loaded with the core purpose, and includes a concrete example and Args section. The phrase 'most powerful' is promotional and adds little, but overall every sentence earns its place.

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?

For a powerful batch tool with no annotations, no output schema, and zero schema descriptions, the description is too thin: it doesn't specify valid command types, error semantics, return values, or limits. An agent cannot fully determine how to construct a correct sequence from this definition alone.

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?

The schema only says 'commands' is an array of objects, so the description's 'List of command dicts with type and params' plus example adds real meaning. It still doesn't enumerate valid command types or parameter schemas, leaving the agent to discover those elsewhere.

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 names a specific action ('execute a sequence of Blender commands in one call') and clarifies it is a batch tool that chains any operations, which separates it from single-operation siblings. It doesn't explicitly contrast with execute_blender_code or run_operator, so it stops short of full sibling differentiation.

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?

The phrase 'batch tool' and 'chains any operations' imply it is for executing multiple commands together, but there is no explicit when-to-use/when-not-to-use guidance or named alternatives. An agent must infer that single operations should go to dedicated tools.

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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