List Chapter Snapshots. Gets information about all the snapshots of a chapter. Each snapshot can be downloaded as audio. Whenever a chapter is converted a snapshot will automatically be created.
Bulk support: accepts project_ids, chapter_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| chapter_id | Yes | ||
| project_id | Yes | ||
| chapter_ids | No | ||
| project_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 include readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the description doesn't need to reiterate those. It adds the behavioral note that snapshots are auto-created on conversion, which is useful. However, it doesn't disclose any additional limitations, like pagination, ordering, or potential heavy payloads.
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 three sentences, front-loaded with the main purpose. It's concise and doesn't waste words, though the bulk support note could be integrated more clearly.
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 low schema coverage and no output schema, the description leaves gaps. It doesn't describe the shape of the returned snapshot information or any sorting/pagination. However, as a list-with-automatic-snapshots tool, it covers core intent but not edge cases around requirements.
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 coverage is 0%, meaning the description provides no parameter semantics. The description mentions the concept of chapter IDs and project IDs via the bulk arrays, but does not clarify the distinction between single (project_id, chapter_id) and bulk (project_ids, chapter_ids) usage, nor does it explain whether both are required. With 5 params and 2 required, the agent is left to infer the parameter relationships.
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 provides a clear verb+resource combination: 'List Chapter Snapshots' and 'Gets information about all the snapshots of a chapter'. It clearly distinguishes from sibling tools like 'elevenlabs_get_chapters' which lists chapters, and 'elevenlabs_get_chapter_snapshot_endpoint' which likely gets a single snapshot. The added 'Bulk support' mention clarifies its batch capability.
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 states the primary use case (list all snapshots for a chapter) and mentions bulk support, but does not explicitly say when to use this over the single-snapshot endpoint or when not to use it. It lacks exclusions or alternatives, though it implies it's the listing variant.
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.