tdmcp

Animate parameter

animate_parameter

Animate node parameters over time with an LFO, binding them to oscillate between min and max. Create automated movement without manual keyframing.

Instructions

Drive one or more node parameters over time with an LFO (sine/triangle/ramp/square/pulse/random). Creates an LFO CHOP and binds each target so it oscillates between min and max with the given period — movement without manual keyframing.

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": {
      "targets": "<targets>"
    },
    "name": "animate_parameter"
  }
}

Input Schema

Name	Required	Description	Default
`max`	No	High end of the value sweep.
`min`	No	Low end of the value sweep.
`name`	No	Name for the LFO CHOP.	lfo_anim
`targets`	Yes	Parameters to animate, each written as 'nodePath.parName' (e.g. '/project1/sys/blur1.size'). Each is switched to expression mode so it tracks the oscillator live.
`waveform`	No	Oscillator shape. Every waveform sweeps the full min–max range.	sine
`container_path`	No	Where to create the LFO CHOP; defaults to the first target's parent network.
`period_seconds`	No	Seconds for one full cycle (lower = faster).

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.13.1Aug 2, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Beyond annotations (readOnly=false, openWorldHint=true), the description reveals side effects: it creates an LFO CHOP, binds targets, and switches each parameter to expression mode. This provides significant behavioral context not captured by annotations alone.

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?

Two sentences, front-loaded with the main action, then the mechanism and benefit. No unnecessary words.

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?

Given the tool's complexity and lack of output schema, the description covers the process, the target format, and the result. It is self-contained and sufficient for an agent to know what happens upon invocation.

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 description coverage is 100%, so the baseline is 3. The description references min, max, and period but does not add additional detail beyond the schema's own parameter descriptions.

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 clearly states the verb 'Drive' and resource 'node parameters over time with an LFO', specifies the mechanism (creates an LFO CHOP and binds each target) and lists waveform types. This distinguishes it from sibling animation/parameter tools such as pulse_td_parameter or set_parameter_expression.

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 implies usage for LFO-based animation without manual keyframing, and contrasts with keyframing. It does not explicitly name alternative tools or state when not to use, but the intended context is clear.

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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