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ElevenLabs MCP Server

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by elevenlabs

create_composition_plan

Read-only

Generate a composition plan for music generation, defining segments, styles, and timing to guide the music creation process.

Instructions

Create a composition plan for music generation. Usage of this endpoint does not cost any credits but is subject to rate limiting depending on your tier. Composition plans can be used when generating music with the compose_music tool.

The returned plan shape depends on model_id:
- music_v2 (default): `{"chunks": [GenerationChunk | AudioRefChunk, ...]}`. Each GenerationChunk has `text`, `duration_ms`, `positive_styles`, `negative_styles`, `context_adherence` and optional `conditioning_ref` + `condition_strength`. AudioRefChunks reference a stored song via `song_id` and `range: {start_ms, end_ms}` for inpainting.
- music_v1: `{"positive_global_styles": [...], "negative_global_styles": [...], "sections": [...]}`.

Args:
    prompt: Prompt to create a composition plan for
    music_length_ms: The length of the composition plan to generate in milliseconds. Must be between 10000ms and 300000ms. Optional - if not provided, the model will choose a length based on the prompt.
    source_composition_plan: An optional composition plan dict to use as a source for the new composition plan. Should match the shape of the model_id you request.
    model_id: Which music model to plan for. One of "music_v1" or "music_v2". Defaults to "music_v2".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYes
music_length_msNo
source_composition_planNo
model_idNomusic_v2
Behavior4/5

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

Annotations indicate readOnlyHint=true and openWorldHint=true, and the description confirms no credit cost and details output shapes. This provides sufficient transparency beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a summary, usage note, return shape details, and parameter list. Each sentence adds value, though slightly verbose.

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 no output schema, the description provides detailed return shapes for both model variants. It covers all parameters and usage context, making it complete for the tool's complexity.

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?

With 0% schema description coverage, the description fully compensates by explaining each parameter, including the range for music_length_ms and the model_id enum values.

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 'Create a composition plan for music generation' and explains how it differs from sibling tools like compose_music, including the relationship between planning and generation.

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 mentions it costs no credits but is rate-limited, and that the plan can be used with compose_music. It also explains optional parameters like source_composition_plan, but does not explicitly state when not to use it.

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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