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ableton-mcp-lofifren

ableton-mcp-lofifren

by LofiFren
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

batch_commands

Execute multiple Ableton commands atomically in one round-trip, with a single undo for the entire batch and early exit on failure.

Instructions

Execute a list of commands in a single round-trip to Ableton.

The whole batch runs atomically inside one main-thread closure, so a single subsequent undo reverts the entire sequence. Execution stops at the first failure and partial results are returned.

Parameters:

  • commands: list of {"type": str, "params": dict}. Allowed types are any modifying or read-only command (e.g. create_midi_track, add_notes_to_clip, set_track_volume). Nested batch_commands is not allowed.

Returns a JSON object with:

  • results: list of per-command {"status", "result" or "message"}

  • executed: how many ran (including the failing one if any)

  • total: how many were submitted

  • failed_at: index of the failure or null

  • error: failure message or null

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": {
      "commands": "<commands>"
    },
    "name": "batch_commands"
  }
}

Input Schema

NameRequiredDescriptionDefault
commandsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A4.8/5.0
Behavior5/5

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

No annotations provided, so the description fully covers behavior: atomicity, undo behavior, failure handling, and return structure. It clearly states the batch runs atomically and stops at first failure.

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 concise and well-structured: purpose first, then atomicity, failure, parameter details, and return shape. Every sentence adds value.

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?

Despite no output schema, the description explains the return object fully with fields like results, executed, total, failed_at, error. It covers all relevant aspects for a batch execution tool.

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 provides detailed parameter semantics: 'commands: list of {"type": str, "params": dict}' with allowed types and prohibition of nesting. This adds significant meaning beyond the bare schema.

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: 'Execute a list of commands in a single round-trip to Ableton.' It specifies atomic execution and distinguishes from sibling tools which are individual commands.

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 explains when to use (batch with atomic undo) and constraints (stops on first failure, no nesting). It implies alternatives by listing sibling tools but does not explicitly state when not to use.

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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