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ChengZiiii

opera-houdini-mcp

by ChengZiiii

start_render

Synchronously starts a ROP render for a specified node, with optional frame range and policy renderer filtering. Returns status, state, elapsed time, and rendered frame range.

Instructions

同步启动一次 ROP 渲染;四层防御见 _render_jobs.start_render

Args:
    node_path: 真实 ROP 节点路径(如 ``/out/mantra1``)。
    policy_renderer: 必填提示,bridge Layer 1 用其初筛(``mantra`` /
        ``opengl`` / ``karma_cpu`` / ``karma_xpu``);不替换真实
        node 推断。
    frame_range: 可选 2 或 3 元 ``[start, end[, inc]]``,缺省走
        ROP 自身设置。
    consent_token: 可选,karma 路径重调时携带。

Returns:
    dict: 直接 relay server 响应;blocked 时为 redirect / interrupt /
    error 字典;正常完成时为 ``status=success`` 含
    ``state / elapsed / frame_range``。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
node_pathYes
frame_rangeNo
consent_tokenNo
policy_rendererYes
Behavior5/5

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

With no annotations, the description carries the disclosure burden. It reveals that rendering is synchronous, that there's a four-layer defense (though specifics are referenced to an internal function), and describes blocked behavior (redirect/interrupt/error dict) and success return fields (state/elapsed/frame_range). This is strong behavioral transparency for a tool of this complexity.

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?

The description is well-structured with Args/Returns sections and front-loads the core purpose. It is appropriately sized for a 4-parameter tool with caveats. However, the reference to `_render_jobs.start_render` is internal and adds mild noise, keeping it from being maximally concise.

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

Completeness4/5

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

Given the tool's complexity and lack of output schema, the description covers all parameters, return behavior, and blocking semantics. It is largely complete, but the 'four-layer defense' is referenced rather than explained, leaving some operational context unspecified. This is a minor gap for an agent making decisions.

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?

Schema coverage is 0%, but the description thoroughly explains each parameter: node_path (real ROP path with example), policy_renderer (required, screening role, allowed values), frame_range (optional format), and consent_token (purpose). This fully compensates for the lack of schema descriptions and adds semantics beyond mere type information.

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?

The description clearly states the tool's purpose: '同步启动一次 ROP 渲染' (synchronously start a ROP render), identifying the action (start) and resource (ROP render). It distinguishes from sibling render tools like render_single_view by explicitly mentioning ROP, making it clear this is for render nodes, not view renders.

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

Usage Guidelines4/5

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

The description provides context on usage: it's synchronous, involves a four-layer defense, and policy_renderer is required for screening. However, it doesn't explicitly state when to use this tool over alternatives or when not to use it. The context is clear enough that an agent can infer appropriate usage, but exclusions are absent.

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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