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TypeWolf

lmms-mcp

by TypeWolf

add_automation

Create automation curves to control LMMS parameters over time—ramp song tempo, adjust track volume, panning, or mixer levels with smooth or stepped transitions.

Instructions

Create an automation curve that controls a parameter over time.

Automatable targets:

  • song + tempo: Song BPM (e.g. tempo ramps)

  • song + master_volume / master_pitch

  • track + volume / panning (target_index = track index)

  • mixer + volume (target_index = mixer channel number)

Args: target_type: "song", "track" or "mixer" param: Parameter name (see above) points: Curve points as list of {"bar": float, "value": float}. Example: [{"bar": 0, "value": 120}, {"bar": 8, "value": 140}] target_index: Track index or mixer channel (ignored for "song") name: Automation name (defaults to parameter name) smooth: True = smooth curves (cubic), False = linear steps

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
paramYes
pointsYes
smoothNo
target_typeYes
target_indexNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior2/5

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

With no annotations, the description carries the full burden. It explains the action but omits side effects, prerequisites (e.g., whether an existing track is required), or whether existing automation is replaced. It also does not describe error behavior or limitations.

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 well-structured, using bullet points for targets and parameters. It is concise, with no unnecessary verbosity, and each piece of information is relevant and directly tied to the tool's operation.

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

Completeness3/5

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

The description covers the core action and parameters but does not mention preconditions (e.g., an active project) or what the tool returns. Although an output schema exists, it is not shown, and the description alone does not provide complete context for a successful call.

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?

The description thoroughly explains every parameter: 'target_type' with allowed values, 'param' with examples, 'points' with the structure and example, 'target_index' with its meaning for each target type, 'name' with default behavior, and 'smooth' with true/false semantics. This fully compensates for the schema's lack of descriptions (0% coverage).

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 function: 'Create an automation curve that controls a parameter over time.' It lists all automatible targets (song, track, mixer) and parameters, which distinguishes it from the sibling tool 'add_automation_track' that likely creates a track instead of a curve.

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 implicit guidance by enumerating the targets and parameters, making it clear this is for creating automation curves. However, it does not explicitly compare to 'add_automation_track' or state when to prefer this tool, so it falls short of full explicitness.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.