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Starrylyn

AutoGLM ASR MCP Server

by Starrylyn
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.2.2

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: transcribe_audio performs the core speech-to-text conversion, while get_audio_info retrieves file metadata for pre-checking. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun snake_case convention (transcribe_audio, get_audio_info), making the API predictable and easy to navigate.

    Tool Count3/5

    The server has only 2 tools, which feels slightly thin for a dedicated ASR service. However, the tools cover the essential workflow (transcription + metadata check), so the count is not inappropriate.

    Completeness4/5

    The core transcription workflow is fully covered, and get_audio_info enables users to verify audio properties before transcribing. Minor gaps such as listing supported formats or job management exist, but these are not critical for the server's stated purpose.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • Last stable release on
    • 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

  • Behavior4/5

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

    With no annotations provided, the description carries the burden. 'Get information' clearly implies a read-only operation, and it discloses the specific information retrieved (duration, format). No side effects or limitations are mentioned, but for a simple metadata retrieval tool, this is sufficiently transparent.

    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?

    Two sentences, front-loaded with the purpose, and no wasted words. Every sentence earns its place, and it reads naturally.

    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?

    For a simple one-parameter tool with no output schema, the description fully covers what it does (returns duration and format), and the use case ('check audio length before transcription') provides valuable context. It is complete and actionable.

    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 coverage is 100% (audio_path described as 'Absolute path to the audio file'). The description adds no extra meaning beyond the schema, but the schema is sufficient. Baseline 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 tool's function: 'Get information about an audio file (duration, format).' This is a specific verb+resource combination, and it distinguishes itself from the sibling tool transcribe_audio by explicitly connecting the use case to transcription preparation.

    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 provides clear usage context with 'Use this to check audio length before transcription.' This implies a specific scenario and distinguishes it from the sibling, though it doesn't explicitly state when not to use it.

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

  • Behavior5/5

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

    With no annotations provided, the description carries the full burden. It fully discloses key behaviors: automatic chunking for long audio, sliding window concurrency, context passing, and return of timing segments. No contradictions with annotations.

    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 well-structured with a one-sentence purpose, a support list, feature bullets, Args, and Returns. Every sentence adds value, and it is easy to scan. No fluff.

    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?

    For a moderately complex tool with no output schema, the description covers supported formats, behavior for long audio, parameter modes, concurrency limits, and the return type. It could add more detail on the structure of timing segments or error handling, but it is complete enough for tool selection and invocation.

    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%, so baseline is 3. The description adds value beyond the schema by explaining trade-offs and recommendations for context_mode (e.g., 'sliding' recommended, 'full_serial' best quality but slow) and clarifying the purpose of max_concurrency. This enhances semantic understanding.

    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 opens with a specific verb and resource: 'Transcribe an audio file to text using AutoGLM ASR.' It clearly distinguishes from sibling tool get_audio_info by focusing on transcription rather than audio metadata.

    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 provides clear context for when to use this tool (any transcription need) and gives detailed parameter guidance (e.g., recommended context_mode). It doesn't explicitly mention alternatives or when not to use the tool, but the sibling tool is obviously different, so the context is sufficient.

    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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