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ChengZiiii

opera-houdini-mcp

by ChengZiiii

execute_houdini_code

Run custom Python code in Houdini to handle operations beyond dedicated tool coverage, with tiered safety policies for controlled execution.

Instructions

Execute arbitrary Python code in Houdini's environment. LAST RESORT:
prefer the dedicated tools (connect_nodes, set_parameters, create_wrangle,
get_geometry_info, ...) — they validate input, report structured errors
and are undoable as a single step. Use this only for operations no
dedicated tool covers.

Args:
    code: Python source to exec inside Houdini.
    policy: "read-only" / "normal" / "privileged" (PR 4 safety policy).
    allow_dangerous: explicit per-call dangerous-code override (privileged only).
    allow_heavy_geometry: explicit per-call heavy-geometry override.
    capture_diff: when True, server snapshots scene state before & after.

Returns status, any stdout/stderr, and an optional audit block.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
policyNonormal
capture_diffNo
allow_dangerousNo
allow_heavy_geometryNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description carries the full burden; it discloses the arbitrary-execution nature, safety policy levels, per-call override flags, and scene snapshot behavior via capture_diff, implying risks compared to validated dedicated tools.

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?

Well-structured intro with warning, followed by a concise Args list; every line adds value without redundancy.

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

Completeness5/5

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

For a complex, high-risk tool, the description covers purpose, safety policy, parameter semantics, and return envelope (status/stdout/stderr/audit block) while an output schema handles precise return structure.

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

Parameters5/5

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

Despite 0% schema coverage, the description explains every parameter's meaning and constraints (policy values, privileged-only for allow_dangerous, heavy-geometry override, capture_diff usage), greatly exceeding schema-only info.

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

Purpose5/5

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

Description states a specific action ('Execute arbitrary Python code in Houdini's environment') and explicitly distinguishes itself from dedicated siblings by labeling itself 'LAST RESORT' and naming alternatives (connect_nodes, set_parameters, create_wrangle).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly instructs when to use ('only for operations no dedicated tool covers') and when not to use ('prefer the dedicated tools'), even naming the specific advantages of alternatives (validation, structured errors, undoability).

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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