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elevenlabs_get_chapter_snapshot_endpoint

Read-onlyIdempotent

Get Chapter Snapshot. Returns the chapter snapshot.

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
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

C2.7/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds only the bulk support note. It doesn't explain what a 'snapshot' is, whether it returns content-only metadata, how snapshots differ from current chapter state, or whether results are cached. For a read operation with readOnly=true annotation, the description should clarify what the snapshot represents and any caveats (e.g., whether it reflects saved state only).

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 very short - two sentences. The first sentence is somewhat tautological ('Get Chapter Snapshot. Returns the chapter snapshot.'), but the bulk support note adds useful information. It's concise and front-loaded, though the tautological repetition wastes a bit of space.

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?

For a tool with 7 parameters, 3 required, and 0% schema coverage, the description is severely incomplete. It doesn't explain what a chapter snapshot is, how it differs from a chapter, when snapshots are created, or whether the bulk parameters replace or supplement the singular ones. The relationship between required singular parameters and optional plural parameters is unclear. With no output schema and no transaction details, this falls short of being fully usable by an agent.

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?

The description provides no parameter-level details beyond the schema. The input schema has 7 parameters with 0% description coverage (schemas themselves have no descriptions). The description only mentions the bulk parameters (project_ids, chapter_ids, chapter_snapshot_ids) without explaining their relationship to the singular required ones. It doesn't clarify whether the singular required parameters are alternatives to the plural bulk parameters, or whether both must be provided together. This is confusing.

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

Purpose3/5

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

The description states 'Get Chapter Snapshot. Returns the chapter snapshot.' This clearly identifies the action (get) and resource (chapter snapshot), but it doesn't distinguish this from the similarly named sibling tool `elevenlabs_get_chapter_snapshot_endpoint` (which appears to be the same name) or `elevenlabs_get_chapter_snapshots`. The purpose is clear but lacks differentiation.

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?

The description mentions 'Bulk support: accepts project_ids, chapter_ids, chapter_snapshot_ids for batched execution.' This provides some guidance on when to use the plural parameters, but it doesn't explain when to use this tool versus other snapshot-related tools like `elevenlabs_get_project_snapshot_endpoint` or `elevenlabs_get_chapter_by_id_endpoint`. No exclusions or alternatives are mentioned.

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.