Skip to main content
Glama

mcp_opendaw_export_effect_chain

Export an audio or MIDI effect chain from an AU as a base64 preset, ready to import into another AU via import_effect_chain.

Instructions

Export an effect chain (audio or MIDI) from an AU as a base64 preset.

Uses PresetEncoder.encodeEffects — serializes the effect chain into a preset binary. Can be imported into another AU via import_effect_chain.

unit_index: AU index to export from. effect_type: "audio" for audio effects, "midi" for MIDI effects.

Returns base64 preset bytes, or error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
unit_indexYes
effect_typeNoaudio

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the full burden and compensates well: it discloses the internal serializer ('Uses PresetEncoder.encodeEffects'), the output format ('base64 preset bytes'), and error behavior ('or error'). It lacks explicit side-effect/permission information, but 'export' implies a read-only operation.

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 and economical: a one-sentence purpose, a brief implementation note, parameter explanations, and return value. Every sentence adds value, with no redundant fluff.

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?

For a simple two-parameter export tool, the description covers everything needed: what it does, how it works, parameter semantics, the complementary import tool, and the return format. The existence of an output schema further reduces the need to elaborate return structures.

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 schema provides only types and a default, but the description explains both parameters in plain language: 'unit_index: AU index to export from' and 'effect_type: "audio" for audio effects, "midi" for MIDI effects.' This fully compensates for the 0% schema description 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 opens with a specific verb and resource: 'Export an effect chain (audio or MIDI) from an AU as a base64 preset.' This clearly states the tool's function and distinguishes it from sibling operations like import_effect_chain or get_effect_chain. The scope (audio or MIDI) is explicitly included.

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 clear context by noting the exported preset 'Can be imported into another AU via import_effect_chain,' establishing the primary use case. It does not explicitly enumerate alternatives or when-not-to-use scenarios, but the purpose and related workflow are transparent.

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

Install Server

Other Tools

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/ameobius-ai/opendaw-mcp'

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