elevenlabs_public_update_order
Update Order. Updates an open order.
Bulk support: accepts order_ids for batched execution.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| request | Yes | ||
| order_id | Yes | ||
| order_ids | No |
Update Order. Updates an open order.
Bulk support: accepts order_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| request | Yes | ||
| order_id | Yes | ||
| order_ids | 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 already indicate readOnly=false, idempotent=false, and destructive=false. The description adds some behavioral context by stating only open orders can be updated and that batched execution via order_ids is supported. It does not disclose side effects, authorization needs, or behavior when an order is not open, but the open-order constraint is useful.
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 and easy to scan, but the opening sentence 'Update Order.' is redundant with the tool name and adds little value. The following sentences are concise and useful, yet the overall structure is not optimally front-loaded because the first sentence is essentially filler.
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?
For a tool with four parameters, a nested required request object, no output schema, and minimal schema descriptions, this description is incomplete. It does not explain how request or order_id should be used, what a successful response looks like, or what prerequisites exist beyond the broad 'open order' statement. The bulk execution note is the only additional context.
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, but it only clarifies the order_ids parameter via 'Bulk support: accepts order_ids for batched execution.' The required order_id and the nested request object (especially its name field) are not explained at all, leaving the agent to infer their meaning from property names alone.
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 a clear action and resource: 'Update Order. Updates an open order.' The phrase 'open order' adds a scope constraint that distinguishes it from related tools like public_submit_order or public_get_order. However, it does not specify what can be updated beyond the general 'order' resource.
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?
The description offers no explicit when-to-use or when-not-to-use guidance, nor does it reference alternatives. It only mentions that order_ids supports batched execution, which hints at a usage scenario but does not explain when to prefer this tool over public_submit_order, public_upsert_order_item, or other order-related siblings.
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.