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strzhao

Video to Text MCP Server

by strzhao
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Server Quality Checklist

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

  • Latest release: v1.0.1

  • Disambiguation4/5

    The two tools have distinct primary purposes: video_to_text handles video files with audio extraction, while voice_to_text handles audio files directly. However, there is some functional overlap in the transcription step, which could cause minor confusion if an agent needs to transcribe audio from a video but chooses the wrong tool. The descriptions help clarify the difference.

    Naming Consistency5/5

    Both tools follow a consistent snake_case naming pattern with a clear 'source_to_text' structure (video_to_text and voice_to_text). This makes them predictable and easy to understand, with no deviations in style or convention across the set.

    Tool Count3/5

    With only 2 tools, the server feels thin for a video-to-text domain, as it lacks operations for managing transcripts (e.g., editing, saving in different formats) or handling video/audio metadata. While the core functionality is covered, the set is borderline minimal and may limit agent workflows.

    Completeness3/5

    The tools cover the basic transcription process from video and audio sources, but there are notable gaps: no tools for updating, deleting, or listing transcripts, and no support for batch processing or different output formats. This could lead to dead ends in more complex agent tasks, though simple transcription needs are met.

  • Average 3.1/5 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 ISC 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. It mentions downloading and transcribing but omits critical details like rate limits, authentication needs, file size constraints, error handling, or output behavior. This leaves significant gaps for a tool that performs external operations.

    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, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded and appropriately sized for its purpose, with no wasted information.

    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 the tool's complexity (external download and transcription), lack of annotations, no output schema, and incomplete parameter documentation, the description is insufficient. It doesn't cover behavioral aspects, output details, or usage context, making it inadequate for safe and effective agent use.

    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 description coverage is 33% (only 'language' has a description), so the description must compensate. It implies the 'url' parameter by mentioning 'audio file from URL' and hints at transcription output, but doesn't explain 'outputFormat' or 'language' beyond the schema. The description adds minimal value, meeting the baseline for low coverage without fully addressing the gaps.

    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 tool's purpose with specific verbs ('download' and 'transcribe') and resources ('audio file from URL' to 'text'). It distinguishes from the sibling 'video_to_text' by specifying audio rather than video. However, it doesn't explicitly mention the sibling differentiation, keeping it at a 4 rather than a 5.

    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 like 'video_to_text' or other transcription methods. It lacks context about prerequisites, limitations, or typical use cases, offering only a basic functional statement without usage instructions.

    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 full burden but only states what the tool does, not how it behaves. It mentions 'save locally' but doesn't disclose where files are saved, file naming, permissions needed, rate limits, error handling, or what 'extract audio' entails technically.

    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, efficient sentence that front-loads the core functionality without unnecessary words. Every phrase ('download a video from URL', 'extract audio', 'transcribe to text', 'save locally') directly contributes to understanding the tool's purpose.

    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 3 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral aspects like file handling, error cases, performance, or output structure, which are critical for an AI agent to use this tool correctly in complex scenarios.

    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 description coverage is 33% (only 'language' has a description), but the description adds no parameter-specific information beyond what's in the schema. It implies 'url' is for video download but doesn't detail supported formats or constraints. Baseline 3 is appropriate as the schema provides some coverage, but the description doesn't compensate for gaps.

    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 specific sequence of actions: download video from URL, extract audio, transcribe to text, and save locally. It uses concrete verbs and distinguishes from the sibling 'voice_to_text' by specifying video as the input source rather than voice/audio.

    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?

    No guidance is provided on when to use this tool versus the sibling 'voice_to_text' or other alternatives. The description implies usage for video-to-text conversion but doesn't specify prerequisites, constraints, or comparative contexts.

    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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