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Cavalry MCP Bridge

Cavalry MCP Bridge

by matthewobanla
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cavalry_set_attributes

Update layer properties in Cavalry by setting attribute values such as position, color, rotation, and opacity using a key-value map of attribute paths.

Instructions

Sets one or more attribute values on an existing layer in Cavalry (e.g. 'material.materialColor', 'position.x', 'scale.x', 'rotation.z', 'opacity').

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": {
      "layerId": "<layerId>",
      "attributes": "<attributes>"
    },
    "name": "cavalry_set_attributes"
  }
}

Input Schema

NameRequiredDescriptionDefault
layerIdYesThe unique layer ID (e.g., 'basicShape#1' or returned layer ID).
attributesYesKey-value map of attribute paths and their target values (e.g., {'position.x': 100, 'position.y': 200, 'material.materialColor': '#FF3366', 'rotation.z': 45}).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are present, so the description carries the full burden. It only states that the tool 'sets' attribute values, but does not disclose whether this is a destructive or additive operation, whether the values are validated, what happens if the layer ID is invalid, or if there are any side effects. For a mutation tool, this is insufficient.

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 a single, well-structured sentence that immediately conveys the core functionality. It includes concrete examples, making it easy to understand without any wasted words.

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 modifies state, has no output schema, and no annotations, the description should cover error conditions, behavior on invalid inputs, and whether the changes are reversible. It only covers the basic action, leaving significant gaps in the agent's understanding of how to use it safely.

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%, so the baseline is 3. The description adds example attribute paths and values, which complements the schema's key-value map description. However, it does not explain the format of attribute paths in depth or any constraints on values, so the added value is modest.

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 action ('Sets one or more attribute values'), the resource ('an existing layer in Cavalry'), and provides specific examples of attribute paths like 'material.materialColor' and 'position.x'. This distinguishes it from siblings like cavalry_get_attributes or cavalry_set_keyframe, which have different purposes.

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

Usage Guidelines3/5

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

The description implies the tool is for modifying existing layers, but it does not explicitly state when to use this tool versus alternatives such as cavalry_set_keyframe (for keyframing) or cavalry_eval_expression (for computed values). No guidance on prerequisites or exclusions is provided.

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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