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Houdini MCP

Houdini MCP

by beatreichenbach
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

sample_geometry

Sample evenly distributed points from a SOP node's geometry by setting sample count and seed. Use it to generate point sets for scattering, instancing, or analysis workflows.

Instructions

Sample evenly distributed points from a SOP node's geometry.

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": {
      "node_path": "<node_path>"
    },
    "name": "sample_geometry"
  }
}

Input Schema

NameRequiredDescriptionDefault
seedNo
node_pathYes
sample_countNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full burden and does not meet it. It does not say the operation is read-only, whether it triggers a cook, how determinism works via the seed, or what happens if the node has no geometry.

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?

A single tight sentence with the core operation front-loaded and no filler. It is efficient, though the brevity comes at the cost of the missing behavioral and parameter detail.

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?

With no annotations, no output schema, and 0% parameter documentation, the definition leaves too much open: point array format, ordering, distribution guarantees, and seed behavior are all unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across three parameters, and the description only hints at what sample_count controls. node_path and especially seed — which governs reproducibility — get no explanation, so the description fails to compensate for the empty schema.

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?

States a specific verb ('Sample') and resource ('points from a SOP node's geometry') with a useful qualifier ('evenly distributed'). It implicitly separates itself from volume-oriented siblings like sample_volume by naming SOP geometry, though it never names an alternative explicitly.

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 on when to prefer this over sample_volume or get_cop_geometry, and no prerequisites stated — nothing about whether the node must be cooked or whether the geometry must already exist before sampling.

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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