Skip to main content
Glama
romanstark

Ableton Maestro

lom_batch

Combine multiple Ableton Live LOM get, set, and call operations into one round trip to reduce overhead. Use atomic mode to stop on first error.

Instructions

Execute multiple raw LOM operations (get, set, call) in a single round trip.

Args:
    ops: List of operation dictionaries ({'op': 'get'|'set'|'call', 'path': ...}).
    atomic: When True, stops execution on the first encountered error.

Returns:
    Dictionary containing ordered results for each operation.

Note:
    A batch runs inside one handler call, so nothing Live recomputes between
    operations is visible to a later operation in the same batch. A read that
    follows a transport jump in the same batch still reports the position from
    before the move. Measured 2026-09-01 against Live 12.4.5: four jumps
    interleaved with four reads returned four identical pre-jump values and no
    error at all. That is the failure shape to expect here. It does not raise; it
    returns a clean set of numbers that look like a measurement of a parameter which
    never changes, and the conclusion drawn from them is wrong. Sample a moving
    transport with one call per position, never inside a batch.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
opsYes
atomicNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does an excellent job: it discloses that Live does not recompute between operations, that reads after transport jumps return stale values, that this specific failure does not raise, and that it produces misleadingly clean results. This is precisely the behavioral context an agent needs.

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 with clear Args, Returns, and Note sections. It is longer than average, but the extra detail about the batch stale-read failure mode earns its place because it prevents a subtle incorrect conclusion.

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, the description covers execution semantics, return shape, atomic behavior, and the critical caveat about transport reads inside batches. Combined with the abundant schea parameter docs, nothing an agent needs to invoke it correctly is missing.

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

Parameters4/5

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

The description adds meaning beyond the schema by explaining ops as a list of operation dictionaries and clarifying atomic as stopping on first error. The schema covers the details of op, path, args, value, and method, so the high-level summary in the description is sufficient.

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 multiple raw LOM operations (get, set, call) in a single round trip. This is a specific verb+resource combination that differentiates it from the sibling single-operation tools lom_get, lom_set, and lom_call.

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 indicates this is for batching multiple operations to reduce round trips and explicitly warns that moving transport should be sampled 'one call per position, never inside a batch.' It could name alternatives more explicitly, but the when-not guidance is strong and actionable.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/romanstark/ableton-maestro'

If you have feedback or need assistance with the MCP directory API, please join our Discord server