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Glama

get_attrib_stats

Compute min, max, mean, and sum statistics for numeric attributes on Houdini nodes across frames or groups. Use to analyze attribute distributions and percentiles.

Instructions

Aggregate statistics for numeric attributes: min, max, mean, sum.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_attrib_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
groupNo
framesNo
uniqueNo
attribsNo
node_pathNo
node_pathsNo
distance_toNo
percentilesNo
attrib_classNopoint

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden and largely drops it. It does not state whether the call is read-only, which defaults apply, how the 'unique' or 'percentiles' modes change the result, or what the response shape looks like; only the output metrics are implied.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The single sentence is front-loaded and free of filler, which is good. But brevity here is achieved by omission rather than by precision, so the terseness actively costs the agent information it needs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

A nine-parameter tool with no annotations, no output schema, and no parameter documentation needs a substantial description, and this one-line entry supplies none of it. Nothing about scoping (group vs node_path vs node_paths), the unique flag, or percentile behavior is recoverable from the definition.

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

Parameters1/5

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

Nine parameters with 0% schema description coverage and the description explains none of them. Terms like group, frames, unique, node_path, node_paths, distance_to, percentiles, and attrib_class are never mentioned, so the agent must guess the meaning and interaction of every argument.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a verb (aggregate statistics) and a resource type (numeric attributes) and enumerates the metrics returned (min, max, mean, sum). However, it never says what the attributes belong to — prims, a group, a node path — and gives no differentiation from a sibling like get_usd_prim_stats, so an agent cannot confidently route between them.

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 exclusion conditions, and no reference to any alternative tool. The agent is left to infer the use case entirely from the tool name and the one-line description.

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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