Generate Composition Plan. Generate a composition plan from a prompt.
Bulk support: accepts model_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| account | No | ||
| model_id | No | ||
| model_ids | No | ||
| music_length_ms | No | ||
| source_composition_plan | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no safety hints (all false), so the description must explain behavior. It mentions generation and bulk execution but omits what the composition plan contains, whether it persists or is ephemeral, cost implications, or non-determinism. The description adds minimal context beyond what the tool name already implies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short with only two sentences. It front-loads the purpose. However, the first sentence 'Generate Composition Plan' is redundant with the second sentence 'Generate a composition plan from a prompt', wasting a few words. Still, it is generally concise and readable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This tool has a large, complex input schema (nested source_composition_plan with sections/chunks), no output schema, and no annotations. The description gives almost no context about expected inputs, outputs, or workflow. It is severely incomplete for an agent to correctly select and invoke this tool, especially among many similar creation tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only explains `prompt` (source of generation) and `model_ids` (batched execution), but leaves `source_composition_plan`, `music_length_ms`, `account`, and `model_id` unexplained. The complex nested source_composition_plan parameter is entirely undocumented in prose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Generate a composition plan from a prompt' which clearly identifies the verb (generate) and resource (composition plan). It is distinct from sound_generation or text_to_speech, though it doesn't explicitly name sibling alternatives. The addition of bulk support via model_ids adds specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like elevenlabs_sound_generation or elevenlabs_generate. The only hint is 'Bulk support: accepts model_ids for batched execution', which implies it supports batched usage but doesn't clarify single vs batch selection or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
There are many tools with overlapping purposes, such as multiple voice retrieval tools (get_voice_by_id, get_voices, get_user_voices_v2, get_library_voices) and several dubbing transcript segment editors with only subtle naming differences. The inclusion of platform-level tools (authenticate, connect, marketplace) alongside ElevenLabs API tools further blurs boundaries.
Naming is highly inconsistent. Most tools have the 'elevenlabs_' prefix, but some do not (authenticate, connect, marketplace, report_bug, show_version, toolkit_info). Several tools have truncated/random suffix names (e.g., elevenlabs_dubbing_target_transcript_segmen_b565e6, elevenlabs_get_pronunciation_dictionary_ver_45baf2), and one tool is in Portuguese (elevenlabs_list_accounts). This mixture of conventions and languages makes the pattern unpredictable.
With 155 tools, the server is extremely bloated. It mixes a comprehensive ElevenLabs API surface with unrelated MCP platform tools (marketplace, authenticate, report_bug, etc.) that belong in a separate toolkit. This is a severe mismatch between the apparent purpose (ElevenLabs audio services) and the sheer number of tools.
The ElevenLabs-specific tools cover a wide range of operations (text-to-speech, voice management, dubbing, pronunciation dictionaries, Studio projects, workspace administration, order management), making it fairly complete for those domains. However, the inclusion of unrelated platform tools and the lack of a clear focus mean that an agent would have difficulty navigating this large surface, and some operations like music finetuning or speech engines appear only partially covered.