Remove Order Item. Removes an order item from an open order.
Bulk support: accepts order_ids, item_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| item_id | Yes | ||
| item_ids | No | ||
| 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 provide no safety hints (all false), so the description carries the full burden. It does disclose that the tool mutates state ('Removes') and constrains to open orders, but it omits details on side effects (e.g., permanence, partial failures in bulk, error handling, permissions). For a mutation tool with zero annotation support, this is insufficient.
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 concise and front-loaded, with the core action in the first sentence. The second sentence adds a useful feature (bulk support) without padding. It is appropriately sized for a simple operation, though it could be slightly more detailed without becoming verbose.
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?
Given the tool has 5 parameters, no schema descriptions, no output schema, and no annotation hints, the description is notably incomplete. It does not explain return values, error behavior, the meaning of each parameter, or the exact semantics of bulk versus single-item execution. Sibling tools exist but no guidance on when to choose this one.
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 explain all parameters. It only mentions 'order_ids, item_ids' for bulk support, but does not clarify the singular required parameters (order_id, item_id) nor the optional 'account' parameter. It also does not explain how singular and plural parameters interact (e.g., whether they are alternatives or parallel arrays). This fails to compensate for the lack of schema descriptions.
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 clearly states the action: 'Remove Order Item' and elaborates as 'Removes an order item from an open order.' This specifies the verb (remove), the resource (order item), and the scope (from an open order), making it distinct from sibling tools like upsert or update. No ambiguity about its purpose.
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 gives a context clue by specifying 'from an open order,' implying it should be used only for orders in a certain state. However, it does not explicitly state when to use this tool over alternatives, nor does it mention any exclusions or prerequisites beyond the open-order condition. The bulk support mention is a feature, not usage guidance.
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.