@runapi.ai/elevenlabs-mcp

Server Quality Checklist

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Latest release: v0.1.7
Disambiguation5/5
Each tool has a clearly distinct purpose: authentication, pricing, task status, and five different ElevenLabs task types (speech, sound, dialogue, transcription, source isolation). No overlap or ambiguity between tools.
Naming Consistency3/5
The tool names mix two patterns: verb_noun (get_task, login, isolate_audio, check_pricing) and noun_to_noun (speech_to_text, text_to_dialogue, text_to_sound, text_to_speech). While each group is internally consistent, the coexistence of two patterns reduces predictability.
Tool Count5/5
With 8 tools, the server is well-scoped for its purpose: covering authentication, pricing, status polling, and five core ElevenLabs task types. No redundant or unnecessary tools; the count fits the ideal range.
Completeness4/5
The set covers the main lifecycle: creating various task types, fetching status/results, authenticating, and checking pricing. Missing a cancel or list operation is a minor gap that agents can work around, but the core workflow is complete.
Average 3.2/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 11 commits in the last 12 weeks
- Last stable release on July 31, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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?
With no annotations, the description carries the full burden for transparency. It discloses the return value (task ID, status, output URLs) and implies an asynchronous task creation, but it doesn't explain that the task may need polling (despite a 'wait' parameter in the schema), how authentication works, or potential side effects. The description is not misleading but is sparse.
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 two sentences, with the action verb front-loaded. It is concise and contains no unnecessary information, though it sacrifices detail for brevity.
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 moderate complexity (10 parameters, no output schema, no annotations), this description is insufficiently complete. It doesn't mention the asynchronous task model, the need for polling or using get_task, the sound-effect model, or parameter semantics. It provides only the core action and return format, which is not enough for an agent to invoke the tool correctly in varied contexts.
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 description coverage is only 20% (wait and model). The description adds no parameter-level explanation beyond the name 'text to sound,' leaving the other 8 parameters undocumented in both the schema and description. This is a significant gap for a tool with 10 parameters.
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 creates an ElevenLabs text-to-sound task on RunAPI and returns a task ID, status, and output URLs. It uses a specific verb ('Create') and resource ('ElevenLabs task on RunAPI (text to sound)'), which distinguishes it from siblings like text_to_speech and text_to_dialogue, though it doesn't explicitly name alternatives.
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 such as text_to_speech or text_to_dialogue. It simply states what the tool does without any context on appropriate use cases, exclusions, or prerequisites.
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 of disclosing behavior. It mentions that the tool returns a task id, status, and output URLs, hinting at asynchronous behavior, but does not explain the task lifecycle, polling requirements, or any side effects. This is minimal and insufficient for a task-based tool.
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 a single concise sentence that gets to the point quickly. It is efficient, though the grammar is slightly awkward ('Create a ElevenLabs'). It earns a high score for brevity.
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 tool's complexity (15 parameters, async task, no output schema, no annotations, multiple siblings), the description is far from complete. It only provides a high-level overview and omits critical details about how to use the tool, what the required 'text' parameter does, and how to handle returned task IDs.
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 description coverage is only 13% (2 of 15 parameters have descriptions). The description adds no parameter meaning, leaving the many parameters (e.g., voice, speed, style, stability) unexplained. It does not compensate for the low schema coverage.
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 action ('Create'), the resource ('ElevenLabs task on RunAPI'), and the domain ('text to speech'). It does not explicitly differentiate from sibling tools like text_to_dialogue or text_to_sound, but the core purpose is unambiguous.
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 is given on when to use this tool versus alternatives. The description does not mention contexts, exclusions, or prerequisites. It only states what the tool does, leaving the agent to infer usage.
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 does disclose that the tool creates a task and returns a task id, status, and output URLs, which implies an asynchronous workflow. However, it omits important behavioral details such as polling behavior, wait semantics, authentication requirements, or side effects.
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 two sentences, front-loaded with the core action and object, and contains no filler. Every word earns its place, making it highly concise and well-structured.
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 high complexity (8 parameters, async task creation) and no output schema or annotations, the description is too sparse. It does not cover parameter semantics, usage scenarios, or operational nuances, making it inadequate for reliable tool selection and invocation.
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 description coverage is only 25% and the description adds no parameter-level information. The 'dialogue' array, wait, stability, language_code, callback_url, timeout_ms, and poll_interval_ms are all unexplained, leaving the agent unable to construct a correct invocation based on the description alone.
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 action ('Create') and resource ('ElevenLabs task on RunAPI'), and clarifies the modality as text-to-dialogue. It distinguishes from speech_to_text but less clearly from sibling text_to_speech and text_to_sound, which is why it doesn't earn a 5.
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 alternatives like text_to_speech or text_to_sound. The description only states the tool's purpose without providing context or exclusions, so the agent receives no decision-making support beyond the tool's name.
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 the full burden. It only states it's a lookup operation, failing to disclose any behavioral traits such as read-only nature, potential costs, or response format.
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 a single, concise sentence that front-loads the purpose. It contains no unnecessary words, though structure is minimal.
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 simple lookup tool with no output schema, the description omits behavioral details and return value information, leaving the agent under-informed for effective use.
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 no extra parameter semantics beyond what the schema already provides.
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's purpose: 'Look up RunAPI pricing for the elevenlabs model line.' It uses a specific verb and resource, distinguishing it from sibling action tools like isolate_audio.
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 is provided on when to use this tool versus alternatives, nor any prerequisites or exclusions. Agents must infer usage from the description alone.
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 is the sole source of behavioral disclosure. It states that the tool returns a task id, status, and output URLs, but omits critical behavioral traits such as asynchronous execution, polling behavior, or side effects. This leaves the agent unprepared for the task lifecycle.
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 one compact sentence that front-loads the key action and return value. It contains no fluff, making it immediately scannable, even though brevity sacrifices depth that other dimensions capture.
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?
The tool has six parameters, no output schema, and no annotations, yet the description fails to explain how parameters interact, what the output URLs contain, or how waiting/polling works. It is a minimal skeleton that leaves substantial gaps for an agent attempting to use the tool correctly.
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 description coverage is only 33% (wait and model have descriptions), and the tool description adds no parameter explanations. Four parameters (timeout_ms, callback_url, poll_interval_ms, source_audio_url) remain semantically opaque despite being necessary for effective invocation.
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 specifies the action 'Create' on the resource 'a ElevenLabs task on RunAPI (isolate audio)', clearly differentiating it from siblings like text_to_speech or text_to_dialogue. The parenthetical 'isolate audio' removes any ambiguity about the task type.
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 alternatives. It does not mention use cases, prerequisites, or why isolate_audio would be preferred over sibling tools. The agent is left to infer situational appropriateness from the name alone.
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 burden of behavioral disclosure. It usefully states that the tool returns a task id, status, and output URLs, implying an asynchronous task model. However, it does not mention the async nature explicitly, nor the wait/polling behavior, auth requirements, or cost implications.
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, containing only two sentences, with the main point front-loaded. However, the grammar error 'a ElevenLabs' slightly detracts from polish, and it could arguably be more directly phrased as 'Transcribe audio using ElevenLabs via RunAPI.' Still, it is efficient and structurally clear.
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?
The tool has moderate complexity (9 parameters, async task creation) and no output schema, so the description must explain return values and behavioral nuances. It does mention the return format (task id, status, output URLs) but omits critical details like how to wait for completion, how callback_url works, what the language_code options are, and what diarize does. This makes the description incomplete for safe, correct use.
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?
The description adds no information about the parameters. The schema has 9 parameters but only one (model) has a description, leaving 78% undocumented. The description does not compensate by explaining source_audio_url, diarize, language_code, or the wait/timeout semantics, which is a significant gap for a tool with this many parameters.
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 identifies the tool as creating an ElevenLabs speech-to-text task on RunAPI, which distinguishes it from sibling tools like text_to_speech and isolate_audio. The verb 'Create' and the resource 'task on RunAPI' are specific, and the parenthetical 'speech to text' confirms the function.
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?
Usage is implied through the name and description—it is for creating speech-to-text tasks—but no explicit guidance is given about when to use this tool versus alternatives like get_task or text_to_speech. There are no exclusionary notes or alternative recommendations.
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. 'Fetch' indicates a read-only operation, which is transparent about its side effects. However, it does not disclose additional behavioral traits like rate limits, authentication needs, or what happens if the task is incomplete or invalid, leaving some gaps.
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, front-loaded sentence with no redundancy. It communicates the core function efficiently, though it contains a minor grammatical error ('a elevenlabs'). Overall, it is 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?
Given the absence of annotations and output schema, the description is moderately complete but leaves questions about return format and error handling. It clearly states what is fetched, but does not explain the structure of the result payload or status values, which is important for an agent to interpret the response correctly.
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 schema description coverage is 100%, with both parameters (task_id and action) already documented. The description adds no extra parameter insights, so the baseline of 3 applies. It does not clarify relationships between parameters or provide usage 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 uses a specific verb ('Fetch') and clearly identifies the resource ('current status and latest result payload for a ElevenLabs task'). It distinguishes itself from sibling action tools (isolate_audio, text_to_speech, etc.) which are async endpoints that create tasks, whereas this retrieves task status.
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 as a polling mechanism for tasks started on async endpoints, but it does not explicitly state when to use this tool versus alternatives. It lacks explicit guidance such as 'use this after initiating an async task' or mentions of when not to use it.
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?
The description discloses the interactive browser-based login flow and file-saving behavior, which is transparent for a credential-setting tool. It does not mention what happens if the config file already exists or if credentials are valid, but the 'force' parameter implies overwrite behavior. No annotations are provided, so the description carries the full burden.
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 conveys all essential information: the action, method, and output location. It is front-loaded with the primary purpose and contains no superfluous 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 the simplicity of the login tool (one parameter, no output schema), the description adequately covers the core behavior. It lacks explicit mention of return values or error states, but the interactive nature makes this less critical. The description is complete enough for an agent to understand and invoke the tool.
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 only parameter 'force' is fully described in the input schema with a clear description and default value. The tool description does not add additional meaning beyond what the schema already provides. Since schema coverage is 100%, a 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 authenticates RunAPI using a browser PKCE flow and saves the API key to a specific config file. The verb 'Authenticate' and resource 'RunAPI' are specific, and the tool is distinct from its siblings (which are unrelated speech/audio tasks).
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 use for initial authentication, but does not explicitly state when to avoid using it (e.g., if already authenticated) or mention that it sets up credentials needed for other tools. However, the context is clear and no alternative exists among siblings.
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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