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Server Quality Checklist

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  • Latest release: v1.1.1

  • Disambiguation3/5

    The tools have some clear distinctions but also notable overlap. Convert_and_resize and resize_image both handle resizing, while convert_and_resize and convert_image both handle format conversion, creating potential confusion about when to use each. However, the descriptions help clarify that convert_and_resize combines both operations, and process_folder is distinct as a batch processor.

    Naming Consistency5/5

    All tool names follow a consistent snake_case pattern with clear verb_noun or verb_noun_noun structures (e.g., convert_and_resize, merge_images, process_folder). This predictability makes the set easy to scan and understand at a glance.

    Tool Count5/5

    With 5 tools, this server is well-scoped for image processing tasks. Each tool serves a distinct purpose (conversion, resizing, merging, batch processing, and a combined operation), and the count feels appropriate without being overwhelming or insufficient for the domain.

    Completeness4/5

    The toolset covers core image operations well, including format conversion, resizing, merging, and batch processing. A minor gap is the lack of tools for more advanced manipulations like cropping, filtering, or metadata editing, but the provided tools support common workflows effectively.

  • Average 3.2/5 across 5 of 5 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.

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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 full burden for behavioral disclosure. It mentions format conversion but doesn't address important behavioral aspects like whether the original file is preserved, what happens with unsupported formats, error handling, or performance characteristics. The description is minimal and lacks operational context.

    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 extremely concise - a single sentence that directly states the tool's function. There's zero waste or unnecessary elaboration. It's front-loaded with the core purpose and efficiently lists the supported formats.

    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 4 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, how errors are handled, or provide context about the conversion process. The agent would need to guess about important operational details.

    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%, providing good documentation for all parameters. The description adds minimal value beyond the schema - it lists the format options but doesn't explain tradeoffs between formats or when to use quality parameter. 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.

    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: converting images between specified formats (PNG, JPEG, GIF, WebP, ICO). It uses a specific verb ('convert') and resource ('image'), but doesn't explicitly differentiate from sibling tools like 'convert_and_resize' or 'resize_image' which suggests overlapping functionality.

    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 alternatives like 'convert_and_resize' or 'resize_image'. The description only states what the tool does, not when it's appropriate or when other tools might be better suited.

    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 the full burden of behavioral disclosure. It mentions the merging action and layout options but omits critical details: whether the operation is destructive to input files, authentication requirements, rate limits, error handling, or output specifics (e.g., image quality, format support). For a mutation tool with zero 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 the core functionality ('Merge multiple images into a single image') and specifies the arrangement methods. There is zero waste, and every word earns its place by clarifying the tool's purpose without redundancy.

    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 (5 parameters, mutation operation) and lack of annotations or output schema, the description is incomplete. It fails to address behavioral aspects like file handling, error cases, or output format details, leaving gaps that could hinder an AI agent's correct invocation. For a tool with no structured safety or output information, more context is needed.

    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 fully documents all 5 parameters. The description adds no additional meaning beyond what the schema provides—it mentions layout options but not details like 'grid' behavior or path requirements. With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.

    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: 'Merge multiple images into a single image by arranging them horizontally, vertically, or in a grid.' It specifies the verb ('merge'), resource ('multiple images'), and method ('arranging them'), but does not explicitly differentiate from sibling tools like 'convert_and_resize' or 'resize_image', which focus on transformation rather than composition.

    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. It lacks context about prerequisites (e.g., image formats supported), exclusions, or comparisons to sibling tools like 'process_folder' for batch operations. Usage is implied only by the tool's name and description.

    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 for behavioral disclosure. It states the tool performs conversion and resizing but doesn't mention whether it overwrites existing files, what happens with aspect ratio conflicts, whether it preserves metadata, or any error conditions. This is inadequate for a mutation tool with 8 parameters.

    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 immediately conveys the core functionality without any wasted words. It's perfectly front-loaded with the essential 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?

    For a complex mutation tool with 8 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, error handling, file system behavior, or how the conversion/resize operations interact. The description should provide more context about this non-trivial operation.

    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 thoroughly. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for high schema coverage.

    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 purpose with specific verbs ('convert' and 'resize') and resource ('image'), and distinguishes it from siblings by emphasizing it's a 'single operation' that combines both functions, unlike separate convert_image and resize_image tools.

    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 implies usage context ('in a single operation') suggesting efficiency benefits over using separate tools, but doesn't explicitly state when to choose this tool versus alternatives like convert_image or resize_image, nor does it mention any prerequisites or exclusions.

    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 lacks critical behavioral details: it doesn't disclose whether the operation is destructive (e.g., overwrites files), what permissions or authentication are needed, rate limits, error handling, or output behavior. The description only covers the basic operation and file filtering, leaving significant gaps for a tool with 9 parameters and batch processing.

    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 and includes a useful clarification about non-image files. Every word earns its place with no redundancy or fluff, making it easy to parse quickly.

    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 (9 parameters, batch processing, no output schema, and no annotations), the description is incomplete. It lacks details on behavioral traits (e.g., destructiveness, error handling), output expectations, and how it differs from sibling tools. For a multi-operation batch tool, more context is needed to guide effective 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 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by implying the tool processes all images in a folder and skips non-images, but doesn't provide additional context on parameter interactions (e.g., how preset overrides width/height) or usage nuances. Baseline 3 is appropriate as the 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 ('Apply a convert, resize, or convert-and-resize operation') and target resource ('to all image files in a folder'), with explicit differentiation from siblings by noting it processes entire folders rather than individual images like convert_image or resize_image. The mention that 'Non-image files are automatically skipped' further clarifies scope.

    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 implies usage context by specifying it operates on folders and skips non-image files, but does not explicitly state when to use this tool versus alternatives like convert_image or resize_image for single files, or how it relates to convert_and_resize (which might be a sibling tool for single images). No explicit exclusions or prerequisites are provided.

    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 the full burden of behavioral disclosure. It mentions that the tool 'preserves the original format', which is a useful behavioral trait, but fails to describe other critical aspects such as whether it modifies the original file, handles errors, supports specific image formats, or has performance implications. For a mutation tool with zero annotation coverage, this leaves significant gaps.

    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 ('resize an image') and includes a key behavioral note ('preserves the original format'). There is zero waste, and every word earns its place by adding value beyond the schema.

    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 moderate complexity (6 parameters, mutation operation) and lack of annotations or output schema, the description is incomplete. It covers the basic purpose and format preservation but omits details on error handling, supported inputs, output behavior, and performance. It's adequate as a minimum viable description but has clear gaps for 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 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by hinting at the 'preset' parameter's purpose ('named preset'), but doesn't provide additional syntax, format details, or usage examples. Baseline 3 is appropriate when the 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 tool's purpose with specific verbs ('resize') and resources ('image'), and distinguishes it from siblings by specifying it preserves the original format, unlike 'convert_and_resize' or 'convert_image' which likely change formats. It explicitly mentions both custom dimensions and named presets.

    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 implies usage by mentioning 'custom dimensions or a named preset', but provides no explicit guidance on when to use this tool versus alternatives like 'convert_and_resize' or 'process_folder'. It doesn't specify prerequisites or exclusions, leaving the agent to infer context from sibling names alone.

    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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