ElevenLabs

by wynandw87

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a unique and clearly distinct purpose: voice cloning, music generation, voice listing, sound effects, speech-to-speech, text-to-speech, transcription, and voice isolation. No two tools overlap in functionality.
Naming Consistency5/5
All tool names follow a consistent snake_case verb_noun pattern (e.g., clone_voice, generate_music, text_to_speech). Even 'transcribe' is a verb and fits the pattern. No mixing of conventions.
Tool Count5/5
With 8 tools, the set is well-scoped for an audio AI server. Each tool serves a distinct audio processing task without being overloaded or insufficient.
Completeness5/5
The tool set covers major audio workflows: generation (music, sound effects), conversion (TTS, speech-to-speech, clone), transcription, listing, and isolation. There are no obvious gaps for common use cases.
Average 3.4/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on February 12, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but the description only mentions 'studio-grade music' and the provider. It does not disclose cost, rate limits, or that it creates a file, leaving critical behavioral traits undocumented.
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 two sentences, front-loaded with the main action. The trigger phrase list is slightly tangential but does not detract. No redundant language.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the core purpose and provider but omits output format details and limitations beyond schema. With no output schema, this is adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear parameter descriptions. The tool description adds no extra meaning beyond the schema, meeting the baseline.
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 it generates studio-grade music from text descriptions using ElevenLabs. It distinguishes from siblings by focusing on 'music' versus speech or sound effects, though explicit differentiation is missing.
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?
No guidance on when to use this tool versus alternatives like sound_effects or text_to_speech. The trigger phrases provide minor usage hints but no exclusions or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden but only notes 'preserves emotion and cadence.' Lacks details on limitations, input formats, or processing effects.
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?
Concise two sentences, front-loaded with purpose. Trigger phrases add slight noise but overall efficient.
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?
With 7 parameters and no output schema, description omits expected output format and parameter context (e.g., meaning of stability or when to use remove_background_noise).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers all parameters fully (100% coverage), so baseline applies. Description adds no extra parameter meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool transforms audio to a different voice while preserving emotion and cadence, distinguishing it from siblings like text_to_speech or transcribe.
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?
Implies usage via trigger phrases but no explicit when-to-use or compare with alternatives like clone_voice or text_to_speech.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description does not disclose behavioral traits such as destructive potential, supported audio formats, processing limitations, or side effects. For a file-processing tool, this is insufficient.
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 purpose. The third sentence on trigger phrases is somewhat extraneous for an agent context but does not detract significantly.
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 the complexity of audio processing and lack of output schema or annotations, the description fails to inform about supported formats, output file type, or processing time, leaving the agent underinformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and already describes both parameters adequately. The description adds no new semantic information beyond what the schema provides, earning the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool isolates vocals from background noise in audio files, using specific verb and resource. It distinguishes from sibling tools like sound_effects and generate_music by focusing on vocal isolation.
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 implies use for cleaning recordings but does not provide when-not-to-use or explicitly contrast with alternative sibling tools. The 'Trigger' phrases are more suited for user interaction than agent decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It mentions 'Saves audio file to disk' but lacks details on side effects (e.g., file overwrite behavior, network dependency, rate limits) and does not state whether the operation is read-only or destructive.
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 two sentences long and front-loaded with the core purpose. The trigger phrases in the second sentence are somewhat redundant but do not harm clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 parameters and no output schema, the description is adequate but missing return value info (e.g., whether it returns the saved file path), error handling, or file type details beyond what the schema provides.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds no extra parameter meaning beyond the schema; it does not explain how parameters affect output or provide examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Convert text to natural speech' and 'Saves audio file to disk', which distinguishes it from siblings like clone_voice, generate_music, or transcribe. The verb 'convert' and resource 'text' to 'speech' is specific.
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 provides trigger phrases for activating the tool but does not explicitly state when to use this tool versus alternatives like speech_to_speech or sound_effects. Usage context is implied but not explicitly guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It only mentions audio duration requirements and the word 'instant' but does not disclose other behavioral traits like persistence of clone, rate limits, destructive actions, or how to use the cloned voice later.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. First sentence states purpose clearly, second gives requirements and trigger phrases. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a cloning tool with 4 params and no output schema, but lacks guidance on post-cloning usage (e.g., how to reference the cloned voice in other tools like text_to_speech). Could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds no additional meaning to parameters beyond what the schema already provides (e.g., trigger phrases are not parameter-specific).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('create') and resource ('instant voice clone'), and mentions the required input (audio samples). It clearly differentiates from sibling tools like text_to_speech or speech_to_speech.
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?
Provides a requirement for audio duration (1-2 minutes) and trigger phrases, but does not explicitly state when to use this tool versus alternatives (e.g., text_to_speech, voice_isolation). Lacks exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states the tool lists and searches, but omits whether it is read-only, what the response format is, or any side effects. This is insufficient.
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 concise at two sentences, front-loading the purpose. It is efficient but could benefit from more structure, such as a brief note about return format.
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?
With no output schema and no annotations, the description should explain what the tool returns (e.g., list of voice objects with IDs, names). It only says 'list and search' without specifying output, which is insufficient for an agent to interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with each parameter already described. The description adds no extra semantic value beyond the schema. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists and searches ElevenLabs voices, using specific verbs and resource. It distinguishes from sibling tools like clone_voice (cloning) and text_to_speech (synthesizing).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes trigger phrases (e.g., 'elevenlabs voices') which provide clear usage context, but lacks explicit when-not-to-use or alternative suggestions. However, the purpose is clear enough to infer appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It mentions optional diarization and 90+ languages, but omits details like API calls, rate limits, or side effects. The behavioral traits are adequately implied but not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise: three sentences front-loaded with the main purpose, additional capabilities, and trigger phrases. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters and no output schema or annotations, the description covers core functionality but lacks details on output format, error handling, or prerequisites. Adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds minimal context beyond the schema (e.g., 'optional speaker diarization' mirrors the 'diarize' parameter). Does not significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'transcribe' and resource 'audio to text' using a specific service (ElevenLabs Scribe). It differentiates from sibling tools like voice isolation or text-to-speech by focusing on transcription.
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?
No guidance on when to use this tool versus alternatives such as speech_to_speech or voice_isolation. The description only provides trigger phrases but lacks context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must disclose behavioral traits. It only states 'Generate sound effects' without mentioning mutability, API key requirements, rate limits, or any side effects. This is insufficient for an AI agent to understand the tool's behavior beyond the basic action.
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 concise, consisting of two sentences. It front-loads the main purpose, followed by context and triggers. No unnecessary words, but it could include more behavioral details without being wordy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema and no annotations. The description covers purpose and use cases adequately, but lacks details about output (audio file path, format) and the auto-save behavior noted in save_path's schema description. It is minimally complete for a simple tool but has gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear descriptions for all 4 parameters. The description adds no additional meaning beyond the schema; it repeats 'text descriptions' but does not elaborate on duration or prompt_influence. Baseline 3 for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'Generate sound effects from text descriptions using ElevenLabs.' This specifies the verb (generate), resource (sound effects), and source (text), making the tool's purpose unambiguous. It is distinct from siblings like text_to_speech or generate_music, though it does not explicitly contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lists use cases ('Great for game audio, video production, and creative projects') and trigger phrases, providing context for when to use. However, it does not explicitly state when not to use or provide alternatives, such as recommending generate_music for music.
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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