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vishalguptax

media-context-mcp

by vishalguptax
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TypeScript
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Server Quality Checklist

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

  • Latest release: v0.1.4

  • Disambiguation5/5

    The two tools have completely distinct purposes: analyze_media handles media content analysis, while check_media_deps checks for required external binaries. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern using snake_case (analyze_media, check_media_deps), making them predictable and easy to understand.

    Tool Count4/5

    With only two tools, the set is minimal but appropriate for a focused utility server. The main tool is comprehensive, and the second tool provides essential support. A slightly larger set might allow more granular operations, but the current count is not problematic.

    Completeness5/5

    The analyze_media tool covers video, audio, and image analysis comprehensively with multiple modes and options. The check_media_deps tool fills the essential dependency-checking role. No obvious gaps are present for the server's stated purpose.

  • Average 4.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 32 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

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

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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?

    No annotations provided, so description carries full burden. It discloses local processing, no paid APIs, and default behaviors for modes. However, it could be more explicit about output format (e.g., what the tool returns) and potential side effects like file generation.

    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 a clear summary, media-type breakdown, and use-case examples. It is detailed but not excessively long. Minor redundancy could be trimmed, but overall efficient.

    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 complexity (23 params, no output schema), the description covers most aspects: purpose, mode usage, parameter guidance, and example workflows. It lacks explicit description of the output format (e.g., what is returned as 'context'), but the 'compact context' phrasing implies model-readable text/images.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    While schema coverage is 100%, the description adds significant value by explaining parameter interactions (e.g., crop + filmstrip, fps + filmstrip) and providing concrete examples for transient glitch detection. It clarifies that detail overrides unset fields and that OCR implies detail:high unless set.

    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 converts local media files or URLs into compact context for models, covering video, audio, and image. It distinguishes video modes (sheet, frames, scenes, filmstrip) and provides specific use-case examples, effectively differentiating from sibling tool check_media_deps.

    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?

    Explicit guidance for when to use specific modes: 'For app/screen recordings use detail:'high' + ocr:true', 'To catch a transient UI glitch ... use mode:'filmstrip' with ...', 'Use the cheap default for everything else.' Also advises to 'Pass context to frame the analysis.'

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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden. It clearly states the tool is a read-only diagnostic check. It does not mention any destructive behavior, which is appropriate. A minor gap is the lack of details about the output format, but for a simple reporting tool this is sufficient.

    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, no wasted words. The first sentence states the purpose, the second gives usage guidance. Front-loaded and efficient.

    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 the tool's simplicity (no parameters, no output schema), the description is complete. It explains what it does and when to use it. No additional information is needed.

    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 tool has zero parameters, and schema description coverage is 100% by default. The description adds value by listing the specific binaries checked, which provides context beyond the empty 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 uses a specific verb 'Report' and clearly identifies the resource: availability of external binaries (ffmpeg, ffprobe, yt-dlp, whisper, tesseract). It also distinguishes itself from the sibling tool 'analyze_media' by providing a usage context.

    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 description explicitly states 'Call this first if analyze_media fails with a missing-binary error.' This provides clear guidance on when to use this tool and implies when not to use it (when no error occurs).

    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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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.

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