Update Workspace Webhook. Update the specified workspace webhook
Bulk support: accepts webhook_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| events | No | ||
| account | No | ||
| webhook_id | Yes | ||
| is_disabled | Yes | ||
| webhook_ids | No | ||
| retry_enabled | No | ||
| request_headers | 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 indicate a non-read, non-destructive write operation, and the description adds only the bulk execution trait. It does not disclose what the update actually changes, whether it is reversible, authentication requirements, or how partial updates behave.
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 front-loaded, but the opening sentence 'Update Workspace Webhook' is redundant with both the tool name and the following sentence. The bulk support note adds value but the duplicated phrasing prevents a higher score.
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 an 8-parameter update tool with no output schema and no schema-level descriptions, this description is too sparse. It does not explain which fields are updatable, how bulk mode interacts with required parameters, or what the response format will be.
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 should compensate, but it only clarifies webhook_ids for batched execution. The remaining seven parameters—name, is_disabled, events, account, retry_enabled, request_headers, and webhook_id—are left entirely undefined.
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 'Update the specified workspace webhook', identifying both the verb and the resource. The 'Bulk support' note further differentiates this from the sibling create/delete/get webhook tools.
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 bulk support note implies batched usage via webhook_ids, but the description does not explicitly state when to use this tool versus creating, deleting, or fetching webhooks. Usage context is only implied by the tool name and the bulk execution hint.
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.