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Houdini MCP

Houdini MCP

by beatreichenbach
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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evaluate_expression

Evaluate Houdini expressions and return the computed result, letting MCP clients query values or run HScript expressions during Houdini workflows.

Instructions

Evaluate an expression in Houdini and return its result.

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": {
      "expression": "<expression>"
    },
    "name": "evaluate_expression"
  }
}

Input Schema

NameRequiredDescriptionDefault
languageNohscript
expressionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.6/5.0
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 does not disclose whether evaluation can have side effects (Houdini expressions may invoke functions or Python code), whether errors raise or return, what form the result takes, or any permission/auth constraints.

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?

A single front-loaded sentence with no filler, which is good, but it is arguably too sparse for a tool with two undocumented parameters, leaving the definition under-specified rather than tight.

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 zero parameter documentation, the definition should explain the language options, result format, and failure behavior. None of that is present, so an agent cannot call this confidently for anything beyond the simplest case.

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 the description must compensate and does not. The 'language' parameter (default 'hscript', implying alternatives such as Python/VEX) is never mentioned, and 'expression' syntax or escaping expectations are not explained.

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 and resource ('Evaluate an expression') plus the domain ('in Houdini') and that a result is returned. It does not distinguish itself from siblings like get_expression, set_expression, or create_vex_expression, so an agent has to infer which expression tool it wants.

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 guidance on when to use this versus the expression-related siblings, and no prerequisites or exclusions are stated. The agent must guess whether this is for reading, testing, or authoring expressions.

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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