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YuvrajSinghBhadoria2

OpenCode Voice MCP Server

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation3/5

    voice_transcribe and voice_type both perform recording and transcription, differing only in whether the result is typed into the active window. This creates some overlap, though voice_status is clearly distinct. Descriptions clarify the intended use.

    Naming Consistency4/5

    The voice_ prefix is consistent across all tools, but the suffix alternates between a verb (transcribe, type) and a noun (status), slightly breaking the uniform verb pattern. Overall, the naming is predictable and readable.

    Tool Count5/5

    With only three tools, the server is tightly scoped to the core voice capture and transcription workflows. Each tool serves a clear purpose, and the count feels appropriate for the narrow domain.

    Completeness4/5

    The server covers the primary actions of transcribing and typing, plus a status check. Missing optional features like language selection or audio device configuration, but these are not essential to the main workflow.

  • Average 3.6/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 8 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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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 provided, the description carries the full burden of behavioral disclosure. 'Check' implies a read-only operation, but it does not specify side effects, output format, or error behavior. The lack of detail about what the tool returns or how it behaves is a significant gap.

    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 directly states the tool's function. It contains no unnecessary words or repetition, making it efficient and front-loaded.

    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 tool's simplicity (0 parameters, no output schema), the description is adequate but has gaps. It does not explain what 'available' means or what format the result takes, which is left to the agent to infer. With no annotations or output schema, more detail on the return value would improve 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?

    The input schema is empty, so there are no parameters to explain. Baseline for 0 params is 4, and the description is not required to add parameter details. The description adds meaning beyond the schema by clarifying the tool's purpose.

    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: checking availability of voice recording and transcription. The verb 'check' and the resource 'voice recording and transcription' are specific, and the purpose is distinct from sibling tools like voice_transcribe and voice_type, which perform actions rather than check status.

    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 does not provide any guidance on when to use this tool versus alternatives. It does not mention prerequisites, context, or exclusions. The sibling tool names imply a distinction, but the description itself offers no usage direction.

    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?

    No annotations are provided, so the description must carry the transparency burden. It discloses only the primary behavior (recording and transcribing) but omits any prerequisites, such as microphone permissions, whether audio is stored, error handling, or potential side effects. This is a significant gap for a tool that records audio.

    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 concise, consisting of two short sentences: 'Record audio from microphone and transcribe to text. Returns the transcribed text.' No unnecessary information is included, and the main action is front-loaded.

    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 has only two optional parameters and no output schema, so the description is mostly adequate. However, it lacks additional context that would be valuable, such as recording duration defaults, language auto-detection behavior, or any limitations. The schema covers parameter details, but the description remains minimal for a tool with no annotations.

    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 provides descriptions for both parameters (duration and language), achieving 100% schema description coverage. The description adds no additional meaning about these parameters or their behavior, 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 clearly states the action: 'Record audio from microphone and transcribe to text.' It specifies the verb (record/transcribe), resource (microphone), and output (text). This distinguishes it from sibling tools 'voice_status' and 'voice_type', which likely handle different voice-related functions.

    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 a clear usage context: when you need audio transcribed. It does not explicitly mention alternatives or exclusions, but the action is straightforward enough that the use case is evident. Since it says 'Record audio from microphone and transcribe to text,' there is no ambiguity about when to use 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 must carry the burden. It discloses the mutating action by stating it types at the cursor position, but it lacks context about prerequisites like microphone access, failure modes, or side effects beyond typing. This is adequate but not comprehensive.

    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, tightly crafted sentence that efficiently communicates the entire workflow. It is front-loaded with the core action and contains no redundant wording.

    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?

    With no annotations and no output schema, the description should explain return values and operational requirements. It does not mention what the tool returns after typing, nor does it address active-window prerequisites. It covers the main purpose but leaves gaps in resource/return value details.

    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 already provides full descriptions for both parameters (duration and language), with 100% coverage. The description does not add additional semantic meaning, adhering to the baseline for high schema coverage.

    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 states a specific verb+resource: 'Record audio, transcribe to text, and type it at the cursor position in the active window.' This clearly differentiates from siblings like voice_transcribe (which only transcribes) and voice_status (which likely reports 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 for dictation-to-text insertion but does not explicitly state when to choose this over voice_transcribe or any exclusions. It provides context for what the tool does but not guidance on alternatives, so it stays at the 'implied usage' level.

    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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