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elevenlabs_stream_chapter_snapshot_audio

Stream Chapter Audio. Stream the audio from a chapter snapshot. Use GET /v1/studio/projects/{project_id}/chapters/{chapter_id}/snapshots to return the snapshots of a chapter.

Bulk support: accepts project_ids, chapter_ids, chapter_snapshot_ids for batched execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accountNo
chapter_idYes
project_idYes
chapter_idsNo
project_idsNo
convert_to_mpegNo
chapter_snapshot_idYes
chapter_snapshot_idsNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=false, meaning this may not be read-only, but description doesn't clarify what side effects exist (e.g., does streaming consume quota?). It mentions 'Bulk support' but not the behavior of returning audio streams (e.g., format, size limits). The description adds minimal behavioral context beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded with the action ('Stream Chapter Audio.'). The bulk support note is useful. No wasted sentences, but it could be more concise by removing the API endpoint reference which is redundant for the agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 8 parameters with 0% schema coverage, no output schema, and no annotations covering safety, the description is insufficient. It doesn't explain what the return value is (audio stream, binary?), how to handle multiple snapshots, or prerequisites like authentication. The description is adequate for a simple tool but lacks depth for streaming behavior and parameter details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description must explain parameters. It mentions project_ids, chapter_ids, chapter_snapshot_ids in bulk support but doesn't describe the required single parameters like project_id, chapter_id, chapter_snapshot_id, or optional convert_to_mpeg and account. The description adds very little beyond the schema's property names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool streams chapter audio and references the API endpoint for snapshots. It distinguishes from siblings like elevenlabs_stream_project_snapshot_archive_60141b by focusing on chapter snapshots, though it doesn't explicitly name the sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It implies usage: call after getting snapshots via the referenced endpoint. However, it doesn't state when to prefer this over alternatives like elevenlabs_get_chapter_snapshots (which retrieves metadata) or how to handle streaming vs downloading. No exclusions are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

C2.4/5.0
Disambiguation2/5

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 Consistency1/5

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.

Tool Count1/5

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.

Completeness3/5

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.