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Houdini MCP

Houdini MCP

by beatreichenbach
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_attrib_values

Read attribute values from a Houdini node as a flat array and paginate results with start and count for large datasets.

Instructions

Read attribute values as a flat array with pagination.

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>",
      "attrib_name": "<attrib_name>"
    },
    "name": "get_attrib_values"
  }
}

Input Schema

NameRequiredDescriptionDefault
countNo
startNo
node_pathYes
attrib_nameYes
attrib_classNopoint

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 behavioral burden. It discloses the return shape (flat array) and that pagination exists, which is useful, but says nothing about how missing nodes/attributes are handled, whether attrib_class changes the result set, or what the defaults mean.

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 front-loaded sentence with no filler or redundancy. Brevity is efficient, though it comes at the cost of the details noted elsewhere.

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 five-parameter tool with zero schema coverage, no annotations, and no output schema, one sentence is far short of what an agent needs. The description would have to explain parameter meaning and error behavior to be complete.

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%, so all five parameters are undocumented in structured fields. The description only implies that start/count control pagination; node_path, attrib_name, and especially attrib_class (default "point") get no explanation anywhere.

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 verb and resource ("Read attribute values") and adds the return shape ("flat array") and pagination. It does not differentiate from nearby siblings such as get_attrib_stats or get_attribute_info, so an agent cannot tell from the text alone which one to pick.

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?

There is no when-to-use guidance, no mention of when to prefer this over get_attrib_stats or get_attribute_info, and no prerequisites. The read intent is only implied by the verb.

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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