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elevenlabs_get_models

Read-onlyIdempotent

Get Models. Gets a list of available models.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accountNo

Schema Changelog

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

  1. First observed

TDQS

D1.9/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description's 'Gets a list' is consistent but adds zero incremental behavior insight. The description doesn't disclose pagination, potential size of response, auth requirements, or any other behavioral details beyond what annotations already communicate. It does not contradict annotations, but contributes no added behavioral value.

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

Conciseness2/5

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

The description is short but needlessly redundant: 'Get Models.' and 'Gets a list of available models.' say the same thing in two sentences. The brevity is not the issue; the content is pure filler that could be trimmed to nothing without losing information. This is under-specification disguised as conciseness.

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?

Even though this is a simple read-only listing operation with zero required parameters, the description still fails to explain what models are returned, how results are ordered, whether there are usage limits, or how this fits with the rest of the ElevenLabs API. With no output schema and no parameter docs, the description had full burden to compensate and did not.

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

Parameters1/5

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

Schema coverage is 0% (single parameter 'account' is undocumented) and the description provides no explanation of what 'account' means — whether it's a tenant ID, workspace, username, or something else. The description fails to compensate for the complete lack of schema documentation, leaving the agent guessing about valid values for the one optional parameter.

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

Purpose2/5

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

"Get Models. Gets a list of available models" restates the function name 'elevenlabs_get_models' with no new information. While it technically specifies a verb and resource, it is essentially a tautology. It doesn't clarify what 'models' means in the ElevenLabs context (e.g., TTS voices) or how this differs from sibling tools like elevenlabs_get_resource_metadata.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus any of the ~160 sibling alternatives. No mention of prerequisites (e.g., authentication), when NOT to use it, or what distinguishes it from other getters like elevenlabs_get_resource_metadata or elevenlabs_get_voice_by_id. The description provides no operational context whatsoever.

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.