ElevenLabs Voice-to-Voice Agent

by Begwen

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Latest release: v1.0.0
Disambiguation5/5
Each tool targets a distinct operation: listing vs fetching voices, listing models, text-to-speech, speech-to-text, user info, and history. There is no overlap or ambiguity between them.
Naming Consistency4/5
Most tools follow a clear verb_noun pattern like list_voices, get_voice, get_user_info, and get_history. However, text_to_speech and speech_to_text deviate from this pattern as noun-to-noun phrases, though they are internally consistent and readable.
Tool Count5/5
Seven tools is a well-scoped number for a voice/audio agent, covering core operations without bloat. The count is appropriate for the apparent purpose.
Completeness3/5
The set covers TTS, STT, voice listing, model listing, user info, and history, but notably lacks a voice-to-voice conversion tool despite the server name. Voice editing/deletion and history item management are also missing, leaving some workflows incomplete.
Average 3.9/5 across 7 of 7 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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, so the description must carry the full burden of behavioral disclosure. While 'Retrieve' implies a safe read operation, the description omits important details such as the return format, pagination behavior, or whether any rate limits apply. This leaves the agent uncertain about the tool's full behavior.
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?
The description is a single, concise sentence that front-loads the main action and resource. Every word earns its place, and there is no redundancy or unnecessary detail.
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 tool is relatively simple with one optional parameter and no output schema, but the description does not explain what the history items contain or how results are structured. This creates a gap for agents that need to interpret the return value, despite the tool's low complexity.
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?
The input schema fully documents the single 'page_size' parameter with a description, minimum, maximum, and default. The tool description adds no additional parameter semantics beyond what the schema already provides, so the 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 uses a clear verb ('Retrieve') and a specific resource ('history of TTS generations from ElevenLabs'), making the tool's function immediately obvious. It also distinguishes itself from sibling tools like list_voices or text_to_speech by focusing on historical generation records.
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?
The description provides no guidance on when to use this tool versus alternatives. It simply states what it does without mentioning any exclusions, prerequisites, or context that would help an agent decide between this and other tools.
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 carries full burden. It states the action but does not disclose return format, possible errors (e.g., 404), authentication requirements, or rate limits. 'Detailed information' is vague and adds no substantive behavioral detail.
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?
A single, focused sentence that conveys the purpose without wasted words. It is front-loaded and immediately understandable.
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 tool is simple with one parameter and no output schema. The description gives the essential purpose but does not elaborate on what 'detailed information' includes, leaving some ambiguity about the return value. For a minimal viable get-by-id tool, it 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 a single parameter 'voice_id' already clearly described. The description's 'by its ID' adds no extra semantics beyond what the schema provides, thus baseline 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 uses a specific verb ('Get') and identifies the resource ('detailed information about a specific ElevenLabs voice by its ID'), clearly distinguishing it from sibling tools like list_voices which retrieves multiple voices.
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 clearly implies when to use the tool (when you need details on a single voice by its ID) and implicitly contrasts with list_voices, though it does not explicitly mention alternatives or exclusions.
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 for behavioral disclosure. It mentions the model and supported formats but omits return structure, error behavior, or any processing side effects, leaving significant gaps for the agent.
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?
The description is a single sentence that leads with the core action, includes the model name, and adds only the relevant mention of supported formats. No redundant or filler content, so it is highly concise and well-structured.
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 tool is simple with fully documented parameters, but the absence of an output schema means the description could clarify what the transcription returns. It does not address error cases or language auto-detection behavior, leaving some contextual gaps despite its clarity.
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?
All three parameters have detailed descriptions in the schema (100% coverage), so the baseline score applies. The tool description adds no additional parameter-specific meaning beyond the schema, making the schema the primary source of semantic information.
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 the resource 'audio file to text', naming the specific model (scribe_v1). It distinguishes from sibling tools like text_to_speech by indicating directionality, and adds format support (WAV, MP3) for concrete scope.
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 clearly implies when to use this tool: when you have an audio file and need text transcription. It provides clear context but does not explicitly mention alternatives or exclusions, so it stops short of a 5.
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?
No annotations are provided, so the description carries the full burden. It discloses that it saves an MP3 file and returns the file path, but does not mention authentication, rate limits, or file overwrite behavior.
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 concise sentences with no filler. The main verb 'Convert' is front-loaded, and the second sentence adds the key side effect and return value without wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description states the return value (file path) and the primary side effect (saves MP3). Schema covers all parameters. Missing context includes external service usage implications, file location, and any limitations.
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 every parameter described in the schema. The description adds no parameter-specific context beyond what the schema already provides, placing it at the baseline.
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 action 'Convert text to speech using ElevenLabs' and specifies the outcome (saves MP3, returns file path). This distinguishes it from sibling tools like speech_to_text and voice management tools.
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 does not explicitly state when to use this tool over alternatives or mention exclusions. The purpose is straightforward, so usage can be inferred, but no direct guidance is provided.
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 provided, the description carries the full burden for behavioral disclosure. It states the action (list all available models) and the return content (capabilities and languages), but it does not mention pagination, authentication, or any other behavioral traits. This is a minimal but sufficient disclosure for a simple read operation.
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?
The description is a single sentence that starts with the action ('List all available...') and delivers complete information without any waste. Every word contributes to the purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema, the description partially explains return values by mentioning 'capabilities and supported languages.' However, it does not detail response structure or potential edge cases, but for a simple list operation this is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description doesn't need to add parameter context; the empty schema already signals no inputs are required.
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 the specific verb 'List' with the resource 'ElevenLabs TTS models' and specifies scope ('all available') and content ('capabilities and supported languages'). It clearly distinguishes from sibling tools like list_voices, which target voices.
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 usage when you need to enumerate all models, but it does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. Sibling tools like list_voices suggest a need for differentiation, but the description alone doesn't provide it.
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?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns voice IDs, names, and labels, which gives useful output expectations, but it does not mention authentication, pagination, or any side effects. For a simple read-only list, this is adequate but lacks depth.
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?
The description is a single sentence that front-loads the verb and outcome, with no redundant words or filler. It is appropriately brief for such a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's core function and return fields, making it sufficient for a parameterless list operation. The lack of an output schema is mitigated by explicitly stating the returned data types.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is nothing for the description to explain. The schema is trivially 100% covered, and the baseline for zero-parameter tools is 4, which applies here.
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 the specific verb 'List' and identifies the resource as 'voices available in ElevenLabs', making it clear this is a listing operation. This distinguishes it from 'get_voice' (retrieving a single voice) and 'list_models' (listing models).
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 this tool is for fetching all voices, but it does not explicitly mention alternatives or when not to use it. Sibling tool names like 'get_voice' suggest a contrast, but the description does not articulate this; guidance is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It clearly frames the operation as a read-only 'Get' and specifies the data returned, which is sufficient for a simple query. No side effects or additional behaviors are disclosed, but none are expected.
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?
The description is a single focused sentence that begins with the action verb and includes the key output details. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter read tool, the description provides enough context: what is retrieved (user/subscription info) and specific elements (character usage, limits). Without an output schema, this adequately sets expectations for the return payload.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool accepts zero parameters, so there is no parameter semantics to clarify. The description correctly omits parameter details, matching the baseline for parameterless tools.
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 retrieves current ElevenLabs user/subscription information, specifying character usage and limits. This distinguishes it from sibling tools focused on voices, models, and audio operations.
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 implies the tool is for account-level information rather than content operations, making its use case obvious. However, it doesn't explicitly mention when to use it over sibling tools or provide exclusions. This is clear context but not explicit guidelines.
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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