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Blender MCP Ultra

Blender MCP Ultra

by 3bdull4h2008
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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add_fluid_physics

Configure an object for fluid simulation by assigning its domain type (domain, flow, effector, or collision) and choosing between liquid or gas behavior.

Instructions

Add fluid simulation to an object.

Args: object_name: Target object. domain_type: DOMAIN, EFFERENT, FLOW, or COLLISION. fluid_type: LIQUID or GAS.

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": {
      "object_name": "<object_name>"
    },
    "name": "add_fluid_physics"
  }
}

Input Schema

NameRequiredDescriptionDefault
fluid_typeNoLIQUID
domain_typeNoDOMAIN
object_nameYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.0.0

TDQS

C2.9/5.0
Behavior2/5

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

Annotations are absent, so the description carries the full burden of behavioral disclosure. It only states the basic action of adding fluid simulation, with no mention of side effects, prerequisites, or behavior on existing simulations. The term 'EFFERENT' appears to be a misspelling of 'EFFECTOR', potentially misleading the agent.

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?

The description is concise, with a clear one-sentence purpose followed by a compact arg list. It is front-loaded and contains no unnecessary words. The structure is efficient and easy to scan.

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?

Given the absence of annotations and output schema, the description is incomplete. It doesn't explain the meaning of domain_type values, prerequisites (e.g., object type), or what happens to existing physics. The agent may need additional domain knowledge to use this tool correctly.

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 description lists each parameter with allowed values (e.g., domain_type: DOMAIN, EFFERENT, FLOW, COLLISION; fluid_type: LIQUID or GAS), adding meaning beyond the bare schema which has no property descriptions. However, it doesn't explain what each domain_type value represents (e.g., what DOMAIN vs FLOW means), and the apparent typo reduces clarity. It provides basic value constraints but not full semantic depth.

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 states a specific verb and resource: 'Add fluid simulation to an object.' It clearly conveys the tool's purpose and distinguishes it from other physics tools like add_rigid_body and add_cloth_physics, though it doesn't explicitly name alternatives. The intent is unambiguous.

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 on when to use this tool versus alternatives such as add_rigid_body or add_cloth_physics. There are no conditions, exclusions, or context cues. The agent is left to infer applicability without explicit advice.

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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