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yuiseki

Edge-TTS MCP Server

by yuiseki
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one lists available voices, the other converts text to speech. There is no overlap or ambiguity between them, making it easy for an agent to select the correct tool for each task.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (list_voices, text_to_speech). The naming is clear, predictable, and adheres to a uniform style throughout the tool set.

    Tool Count2/5

    With only 2 tools, the server feels thin for a text-to-speech domain. While the tools cover core functionality, typical TTS systems might include additional operations like voice configuration, audio format settings, or batch processing, suggesting the scope is under-served.

    Completeness3/5

    The tools cover basic TTS operations (listing voices and converting text), but there are notable gaps. For example, there is no tool for configuring voice parameters (e.g., pitch, rate) or managing audio output formats, which could limit agent capabilities in more complex scenarios.

  • Average 2.9/5 across 2 of 2 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
    • No stable releases found
    • 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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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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 carries full burden. It states what the tool does but doesn't disclose behavioral traits like whether it's read-only, requires authentication, has rate limits, returns paginated results, or what format the list is in. This leaves significant gaps for an agent to understand how to interact with it.

    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, clear sentence with zero wasted words. It's appropriately sized for a simple tool and front-loaded with the essential information, making it easy to parse quickly.

    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 no annotations, no output schema, and a simple input schema, the description is incomplete. It doesn't explain what the list contains (e.g., voice names, IDs, properties), how results are structured, or any behavioral aspects. For a tool that returns data, more context is needed to use it effectively.

    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 the single parameter 'locale' documented in the schema. The description doesn't add any meaning beyond what the schema provides (e.g., it doesn't explain what happens if locale is omitted or provide examples beyond those in the schema). Baseline 3 is appropriate 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.

    Purpose4/5

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

    The description clearly states the action ('Get a list') and resource ('available voices'), making the purpose immediately understandable. It doesn't differentiate from its sibling 'text_to_speech' tool, which performs a different function (synthesis vs listing), so it doesn't reach the highest score for sibling differentiation.

    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 or in what context. While the sibling tool 'text_to_speech' is clearly for a different purpose, there's no explicit mention of when to use list_voices (e.g., for voice selection before synthesis) or any 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 behavioral disclosure but only states the basic function. It doesn't mention whether this is a read-only or destructive operation, what permissions might be required, rate limits, what format the output takes (audio file, stream, etc.), or any error conditions. For a tool with 7 parameters and no annotation coverage, 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.

    Conciseness5/5

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

    The description is extremely concise at just three words, with zero wasted language. It's front-loaded with the core function and contains no unnecessary elaboration. While this conciseness comes at the cost of completeness, the structure itself is optimal for its length.

    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 tool with 7 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (audio format, file location, success indicators), doesn't provide behavioral context, and offers no usage guidance. The 100% schema coverage helps with parameters, but the overall context for proper tool invocation is 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?

    The description adds no parameter information beyond what's already in the schema (which has 100% coverage). All 7 parameters are documented in the input schema with descriptions, defaults, and types. The description doesn't provide additional context about parameter interactions, validation rules, or examples of usage patterns.

    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 'Convert text to speech' clearly states the tool's function with a specific verb ('convert') and resource ('text to speech'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its sibling 'list_voices' or explain how they differ in functionality.

    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. There's no mention of the sibling tool 'list_voices' or any context about when text-to-speech conversion is appropriate versus listing available voices. The description lacks any usage context or prerequisites.

    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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  • Evaluate tool definition quality.

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