Stream Archive With Studio Project Audio. Returns a compressed archive of the Studio project's audio.
Bulk support: accepts project_ids, project_snapshot_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| project_id | Yes | ||
| project_ids | No | ||
| project_snapshot_id | Yes | ||
| project_snapshot_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?
The annotations are generic and uninformative, so the description carries the burden. It adds useful behavioral context by saying the output is a compressed archive and that batch execution is supported via plural ID parameters. Still, it does not disclose the archive format, whether the response is streamed or fully delivered, or what exact contents are included beyond 'audio.'
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, mostly front-loaded, and the bulk-support note is separated for clarity. It is efficient, though the opening phrase 'Stream Archive With Studio Project Audio' is somewhat redundant with the following sentence.
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 no output schema and five parameters, the description is enough to convey the core purpose and return type, but it leaves important gaps: what a 'project_snapshot' is, how the required parameters interact, and what an agent should do when using the bulk variants. It is minimally complete but not richly actionable.
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 for understanding parameters. It only clarifies that project_ids and project_snapshot_ids are for batched execution, but it does not explain the relationship between project_id and project_snapshot_id, what account is used for, or how required singular parameters interact with the batch arrays. This leaves several parameters ambiguous.
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 that the tool returns a compressed archive of the Studio project's audio, which is a specific verb + resource combination. It also distinguishes itself enough from related snapshot/chapter tools by emphasizing project audio archiving, though it could more explicitly say 'project snapshot audio'.
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 clear general context: use this tool to get a compressed audio archive of a Studio project, and the 'Bulk support' note provides a useful helper for array inputs. However, it does not explicitly explain when to choose this over sibling tools like elevenlabs_stream_chapter_snapshot_audio, nor does it mention when not to use it.
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.