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Glama

ElevenLabs

by io.github.mcp-dir

elevenlabs_generate

Compose Music. Compose a song from a prompt or a composition plan.

Bulk support: accepts model_ids, finetune_ids for batched execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
promptNo
accountNo
model_idNo
model_idsNo
finetune_idNo
lyrics_textNo
finetune_idsNo
music_promptNo
output_formatNo
sign_with_c2paNo
generation_modeNo
music_length_msNo
composition_planNo
finetune_strengthNo
force_instrumentalNo
use_phonetic_namesNo
store_for_inpaintingNo
respect_sections_durationsNo

Schema Changelog

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

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations indicate a write operation (readOnlyHint=false), and the description confirms this by stating it composes music. It adds a useful behavioral detail about bulk execution, but does not disclose return behavior, authentication needs, pricing, or generation side effects beyond the minimal 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 extremely concise, with two sentences and no redundant content. It is front-loaded with the core operation, then the bulk support note adds a useful secondary fact.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a complex 19-parameter tool with no output schema and no parameter documentation. The description covers only top-level entry points and bulk mode, but does not clarify core schema concepts like fine-tune strength, C2PA signing, generation modes, or how outputs are returned, making it insufficient for confident invocation.

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

Parameters2/5

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, but it only names prompt, composition plan, model_ids and finetune_ids. It gives no meaning for the other 15 parameters such as music_prompt, generation_mode, output_format, or muse_strength, leaving the agent to infer or guess.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Compose Music' and 'Compose a song from a prompt or a composition plan', giving a specific verb and resource. It distinguishes itself from sound-generation and text-to-speech tools, though it does not explicitly contrast with the sibling elevenlabs_compose_plan.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool: when the user wants a song generated from a prompt or a composition plan. It also adds bulk execution guidance via model_ids and finetune_ids, but it does not state when to prefer alternatives or provide exclusions.

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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TDQS

C2.4/5.0
Disambiguation2/5

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 Consistency1/5

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.

Tool Count1/5

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.

Completeness3/5

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.

Resources