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AIWerk

@aiwerk/mcp-server-elevenlabs

by AIWerk

get_similar_library_voices

Find library voices matching an audio sample by comparing similarity, with optional top-k and threshold controls. Uses ElevenLabs credits.

Instructions

Get Similar Library Voices Spends ElevenLabs credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_kNoNumber of most similar voices to return. If similarity_threshold is provided, less than this number of voices may be returned. Values range from 1 to 100.
audio_file_pathNoFile for "audio_file". Local path.
audio_file_base64NoBase64 contents for "audio_file". Use this when the server cannot read your local disk.
audio_file_filenameNoFilename to send for "audio_file". Some endpoints infer the audio format from it.
similarity_thresholdNoThreshold for voice similarity between provided sample and library voices. Values range from 0 to 2. The smaller the value the more similar voices will be returned.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, destructiveHint=false, idempotentHint=false, and openWorldHint=true, covering the safety profile. The description adds that the tool spends ElevenLabs credits, a meaningful cost behavior not present in annotations. It does not explain how many credits are consumed, whether the cost is per call, or any auth requirements.

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

Conciseness3/5

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

The description is very short and front-loads the purpose, but it reads as a run-on fragment ('Get Similar Library Voices Spends ElevenLabs credits') with missing punctuation and structure. It is concise but not well-crafted, and the second idea is not cleanly separated.

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?

There is no output schema, so the description should clarify what is returned and what input is required, but it does neither. It fails to explain that an audio sample is needed (via audio_file_path, audio_file_base64, or audio_file_filename) or how top_k and similarity_threshold affect results. Annotations and schema cover some context, but key invocation details are missing.

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 all five parameters are documented in the schema. The description adds no parameter detail beyond the tool name, and provides no explanation of the audio input options or similarity threshold beyond what the schema already states. Baseline 3 applies when the schema does the heavy lifting.

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 a general verb and resource ('Get Similar Library Voices'), but it does not specify what the voices are similar to (a provided audio sample) or differentiate itself from sibling tools like get_similar_voices_for_speaker. The second clause about credits is a cost note, not purpose, leaving the purpose minimally viable.

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, nor any prerequisites or exclusions. The only contextual detail is a cost warning ('Spends ElevenLabs credits'), which is a behavioral note rather than usage guidance.

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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