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tvtdev94

clvoice-mcp

by tvtdev94
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one for dictation and transcription, the other for listing audio devices. No overlap in functionality.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern: 'voice_listen' and 'list_audio_devices', with snake_case and clear action prefixes.

    Tool Count5/5

    Two tools is appropriate for the narrow scope of voice input: one to configure the device and one to capture input. The tool count matches the domain well.

    Completeness4/5

    The server covers the core needs (capture input, list devices). A minor gap is the lack of a tool to set the microphone device directly, but this is handled via environment variable.

  • Average 4.2/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 12 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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  • 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

  • Behavior3/5

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

    With no annotations, the description bears full responsibility. It discloses that the tool does not return a transcript and advises the agent to wait for user edits. However, the phrase 'must NOT be executed' is contradictory to 'after calling,' creating confusion about whether the agent should invoke the 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 relatively concise at three sentences, but the confusing instruction 'must NOT be executed' detracts from clarity and could be rephrased.

    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 purpose, side effect, and usage guidance adequately for a tool with only two parameters and no output schema. However, the ambiguous execution instruction and lack of detail on behavior when language is not supported reduce completeness.

    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?

    Schema coverage is 100%, but the description adds value by noting defaults (15 seconds for 'seconds', 'vi' for 'language') and that 'seconds' is clamped to server maximum. This goes beyond the schema's parameter descriptions.

    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 function: record microphone, transcribe Vietnamese or English speech, and place text into user's input box. It also specifies what it does not do (return transcript) and distinguishes from the sibling tool list_audio_devices.

    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 explicitly says 'Use when the user wants to dictate input' and instructs the agent to confirm briefly and wait for the user's next message after calling. However, it does not mention when to avoid using it (e.g., if user wants to transcribe a file).

    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 states the action (list) and purpose but does not disclose any additional behavioral traits such as side effects, permissions, or return structure. For a simple read-only tool, this is minimally adequate.

    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?

    Single sentence, front-loaded with the verb 'List', no wasted words. Every part earns its place.

    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?

    Given zero parameters, no output schema, and low complexity, the description fully covers what an agent needs to know: it lists device names for a specific purpose. No gaps.

    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?

    No parameters exist, so baseline is 4. The description does not need to add parameter info. It adds value by explaining the purpose, which is sufficient.

    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 it lists available microphone input device names. The verb 'list' matches the resource 'audio devices', and the purpose is specific. Sibling tool voice_listen is distinct, so no confusion.

    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?

    Explicitly says the list is for setting CLVOICE_MIC_DEVICE, providing clear usage context. Does not include when-not or alternatives, but given the simple sibling relationship, it is adequate.

    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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