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matthewobanla

Cavalry MCP Bridge

cavalry_run_script

Execute JavaScript automation scripts in Cavalry using the api.* module to create procedural animations, build node networks, and perform batch updates.

Instructions

Executes a multiline JavaScript automation script inside Cavalry using the api.* module. Use this for complex procedural generation, node network creation, and batch updates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesJavaScript code using Cavalry API methods (e.g. `api.create()`, `api.set()`, `api.connect()`, `api.setKeyframe()`).
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It merely states execution and use cases, without mentioning error handling, idempotency, side effects (e.g., irreversible scene modifications), return value, or execution mode (synchronous/asynchronous). This is a significant gap for a powerful script execution tool.

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?

The description is two sentences, front-loaded with the core action and purpose, followed by concise use cases. No filler or redundant information. Every sentence earns its place.

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?

Given the tool's complexity (executes arbitrary JavaScript) and absence of an output schema, the description lacks crucial operational details: what happens on errors, whether the script can be debugged, if it returns any status, and how it interacts with the scene state. This makes it incomplete for safe and effective agent use.

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

Parameters3/5

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

Schema coverage is 100% and the parameter description already explains the code format with examples (`api.create()`, etc.). The tool description adds use-case context but no new parameter semantics beyond what the schema provides. Baseline 3 per guidelines.

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 executes a multiline JavaScript automation script using the `api.*` module, specifying complex procedural generation, node network creation, and batch updates as use cases. This distinguishes it from sibling tools like `cavalry_create_layer` or `cavalry_set_attributes`, which handle single operations.

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 explicit use cases ('Use this for complex procedural generation, node network creation, and batch updates'), which guides when to select it over simpler single-purpose sibling tools. However, it does not explicitly state when not to use it or list alternatives.

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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