Skip to main content
Glama
fgasparetto

Voice Transcriber MCP Server

by fgasparetto
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v2.1.0

  • Disambiguation5/5

    The two tools target distinct input sources: one for local audio files and one for Google Chat voice messages. Their purposes are clearly separated, leaving no ambiguity for an agent.

    Naming Consistency5/5

    Both tool names follow the identical pattern 'transcribe_<source>', using snake_case and a clear verb-noun structure. Naming is consistent and predictable.

    Tool Count4/5

    With only 2 tools, the set is minimal but matches the narrow scope of transcribing voice from two specific sources. It is slightly underpopulated but not unreasonable.

    Completeness4/5

    The server covers the core need of transcribing audio from local files and Google Chat messages. Minor gaps exist, such as no support for URLs or other chat platforms, but it is complete for its focused purpose.

  • Average 3.8/5 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 must fully disclose behaviors. It mentions the API and model, but omits important details such as file size/duration limits, cost implications, whether the file is uploaded, or any side effects. This lack of transparency is a significant gap for a tool that likely involves network calls and data processing.

    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 (4 sentences) and well-structured with separate sections for arguments and returns. It avoids unnecessary details but includes a helpful list of supported file types. The use of bullet points and clear headers enhances readability.

    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 and the presence of an output schema (which indicates structured return), the description adequately covers the core functionality and parameters. However, it lacks details on error handling, pagination, or additional response fields, leaving gaps for an agent to know what to expect in edge cases.

    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 has 0% description coverage, so the description compensates by explaining each parameter's purpose and the default language. However, it does not specify allowed language codes or file path format, leaving some ambiguity.

    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 verb (transcribe), the resource (local audio file), and the specific model (Groq Whisper API whisper-large-v3). It explicitly lists supported file types and distinguishes from sending a voice message by emphasizing local files.

    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 specifies to use for local audio file transcription, but does not provide guidance on when not to use it or when to choose the sibling tool 'transcribe_voice_message' instead. The context implies local files, but lacks explicit exclusions or alternatives.

    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 carries full burden for behavioral disclosure. However, it only mentions the basic operation and does not disclose authentication, rate limits, side effects, or error handling. The instruction to transcribe immediately adds minimal context.

    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 well-structured with an 'IMPORTANT' section and 'Args' section, but is slightly verbose. It effectively communicates key points without excessive detail. Minor redundancy in the 'Returns' line could be removed.

    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 presence of an output schema (not shown but referenced), the description covers the essential context: when to use, parameter details, and return value description. It lacks error handling or timeout information, but for a simple transcription tool, it is mostly complete.

    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 schema coverage is 0%, but the description compensates by explaining the 'message_url' parameter with example formats (URLs and resource names) and gives the default language and its value ('it' for Italian). This adds meaningful semantics not present in the raw schema.

    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 'Transcribe a voice message from Google Chat', specifying both the action and the resource. It distinguishes from the sibling tool 'transcribe_audio_file' by focusing on Google Chat messages.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The 'IMPORTANT' section explicitly instructs the AI to call this tool automatically when a Google Chat message with an audio attachment is detected, without asking the user. This provides clear when-to-use guidance and contrasts with the sibling tool.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

voice-transcriber-mcp MCP server

Copy to your README.md:

Score Badge

voice-transcriber-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/fgasparetto/voice-transcriber-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server