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jbergant

Image Processor MCP Server

by jbergant
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JavaScript
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation3/5

    The two tools have overlapping purposes—both process images and upload to Vercel Blob—but the descriptions clearly differentiate them by input source (local file vs. URL). This overlap could cause minor confusion, but the distinctions are explicit enough to guide selection.

    Naming Consistency5/5

    Tool names follow a consistent verb_noun pattern with 'process_and_upload_image' as the base, extended with 'from_url' for the second tool. This predictable naming makes it easy to understand the tool set's structure and relationships.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for an 'Image Processor' domain. This minimal set lacks operations like basic processing without upload, downloading images, or managing uploaded files, limiting agent workflows.

    Completeness2/5

    The tool set is severely incomplete for image processing. It covers only upload-oriented workflows, missing essential operations such as standalone processing, format conversion, resizing, or retrieval of images, leaving obvious gaps in the domain.

  • 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

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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?

    No annotations are provided, so the description carries the full burden. It mentions processing (optimize, resize, convert) and uploading, but lacks details on behavioral traits like required permissions, rate limits, error handling, or what happens if processing fails. For a tool that modifies and uploads files with no annotation coverage, this 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, efficient sentence that front-loads key information (process local image, upload to Vercel Blob) and includes essential details (optimize, resize, convert to WebP). Every part earns its place with no wasted words.

    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 complexity (processing and uploading images) and lack of annotations and output schema, the description is moderately complete. It covers the core purpose and distinguishes from siblings, but lacks details on behavioral aspects and output, which are important for a tool with mutation and external integration.

    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 100%, so the schema already documents all parameters. The description adds no specific parameter semantics beyond what the schema provides, such as explaining interactions between parameters (e.g., how width/height affect resizing). Baseline 3 is appropriate when the schema handles parameter documentation.

    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 verb ('process and upload') and resource ('local image file'), and distinguishes from the sibling tool 'process_and_upload_image_from_url' by specifying the source as local rather than URL-based. It explicitly lists the processing operations: optimize, resize, convert to WebP, and the destination: Vercel Blob.

    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 (for local image files) and implies an alternative (the sibling tool for URL-based images). However, it does not explicitly state when NOT to use this tool or mention other potential alternatives beyond the sibling, such as direct upload without processing.

    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 carries the full burden. It discloses key behavioral traits: the tool processes (optimizes, resizes, converts to WebP) and uploads to Vercel Blob. However, it lacks details on permissions, rate limits, error handling, or what happens if upload fails, which are important for a mutation tool.

    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 purpose. Every word earns its place by specifying the action, source, processing steps, and destination without any redundancy or fluff.

    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 complexity (image processing and upload), lack of annotations, and no output schema, the description is minimally adequate. It covers the what and where but misses details on behavioral aspects like success/failure outcomes, which would enhance completeness for a mutation tool.

    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 schema description coverage is 100%, so the schema already documents all parameters. The description does not add any additional meaning or context beyond what the schema provides, such as explaining the optimization process or WebP conversion specifics. Baseline 3 is appropriate when schema does the heavy lifting.

    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 action: 'Process an image from a URL (optimize, resize, convert to WebP) and upload to Vercel Blob'. It uses precise verbs ('process', 'upload'), specifies the resource ('image'), and distinguishes from its sibling 'process_and_upload_image' by explicitly mentioning 'from a URL'.

    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 usage context by specifying 'from a URL', which suggests this tool is for remote images rather than local files. However, it does not explicitly state when to use this versus the sibling tool 'process_and_upload_image' or provide any exclusion criteria, leaving some ambiguity.

    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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