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elevenlabs_public_submit_order

Submit Order. Submits an open order for processing. The order must have at least one item. Once submitted, items can no longer be modified.

Upon submission, the workspace will be charged for the order. The quote is based on information extracted from the uploaded media, such as its duration. The quote may not be available immediately; if you wish to see the quote before submission, you may need to poll the order details until the quote is ready.

Bulk support: accepts order_ids for batched execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accountNo
order_idYes
order_idsNo

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

The description discloses significant behavioral traits beyond the sparse annotations: the workspace will be charged, items become non-modifiable after submission, and the quote may be delayed. These are crucial financial and mutability consequences that the annotations (readOnlyHint:false, destructiveHint:false) do not convey. No contradictions with annotations.

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 concise and well-structured: a clear purpose sentence, necessary preconditions/constraints, important billing and polling context, and a final bulk-support note. Each sentence contributes meaningful information with no redundancy or filler.

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

Completeness4/5

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

The description covers the essential context for a side-effect-heavy operation: preconditions, post-submission immutability, charging behavior, and quote timing. It does not explain the response/return value, and the 'account' parameter remains unaddressed, but the overall operational context is strong enough for an agent to select and invoke the tool correctly.

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

Parameters3/5

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

With 0% schema description coverage, the description must compensate for parameter meaning. It explains order_id through context ('submits an open order') and explicitly introduces order_ids for batched execution. However, the optional 'account' parameter is entirely unexplained, leaving one of three parameters semantically opaque.

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 action: 'Submits an open order for processing.' It identifies the specific resource (order) and distinguishes this from sibling tools like create_order, get_order, and list_orders by focusing on the submission action. Additional details about item modification and bulk support further refine the purpose.

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 gives clear context: the order must be open, have at least one item, and becomes immutable after submission. It also advises polling order details if the quote is needed before submission. However, it does not explicitly name alternative tools or state when not to use this tool, so it falls short of a 5.

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.