Imagician
Imagician is an MCP server for comprehensive image editing operations:
Resize images - Change dimensions with flexible fit options (cover, contain, fill, inside, outside) while preserving aspect ratio
Convert formats - Transform between JPEG, PNG, WebP, and AVIF with adjustable quality settings (1-100)
Crop images - Extract rectangular regions using left, top, width, and height coordinates
Compress images - Reduce file size with quality control (1-100) and progressive encoding for JPEGs
Rotate images - Rotate by any angle in degrees (clockwise for positive values) with customizable background color
Flip images - Mirror horizontally, vertically, or both directions
Extract metadata - Retrieve format, dimensions, color space, and file size information
Batch process - Generate multiple sizes from a single source image with custom suffixes and optional format conversion for responsive web images
Supports JPEG format conversion, compression with quality control, and progressive encoding for image processing operations
Available as an npm package for easy installation and distribution
Built with TypeScript for type-safe image processing operations and development
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Imagicianresize profile.jpg to 800x600 pixels"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Imagician - MCP Image Editor
A Model Context Protocol (MCP) server for comprehensive image editing operations including resizing, format conversion, cropping, compression, and more.
Features
Resize: Change image dimensions with various fit options
Format Conversion: Convert between JPEG, PNG, WebP, and AVIF
Crop: Extract specific regions from images
Compress: Reduce file size with quality control
Rotate: Rotate images by any angle
Flip: Mirror images horizontally or vertically
Batch Processing: Generate multiple sizes from a single image
Metadata: Extract image information and properties
Related MCP server: FFmpeg MCP Tool
Installation
From npm
npm install -g @flowy11/imagicianFrom source
git clone https://github.com/flowy11/imagician.git
cd imagician
npm install
npm run buildUsage with Claude Code
Add to your Claude Code configuration (~/.config/claude/config/settings/mcp-servers.json):
{
"imagician": {
"command": "npx",
"args": ["-y", "@flowy11/imagician"]
}
}Usage with Cursor
Add to your Cursor configuration (~/.cursor/mcp_settings.json):
{
"mcpServers": {
"imagician": {
"command": "npx",
"args": ["-y", "@flowy11/imagician"]
}
}
}Usage with Claude Desktop
Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json):
Quick setup with npx (recommended):
{
"mcpServers": {
"imagician": {
"command": "npx",
"args": ["-y", "@flowy11/imagician"]
}
}
}If installed globally via npm:
{
"mcpServers": {
"imagician": {
"command": "imagician"
}
}
}If installed from source:
{
"mcpServers": {
"imagician": {
"command": "node",
"args": ["/path/to/imagician/dist/index.js"]
}
}
}Available Tools
resize_image
Resize an image to specified dimensions with various fit options.
Parameters:
- inputPath: Path to input image
- outputPath: Path to save resized image
- width: Target width in pixels (optional)
- height: Target height in pixels (optional)
- fit: How to resize (cover, contain, fill, inside, outside)
- preserveAspectRatio: Maintain aspect ratio (default: true)convert_format
Convert between image formats with quality control.
Parameters:
- inputPath: Path to input image
- outputPath: Path to save converted image
- format: Target format (jpeg, png, webp, avif)
- quality: Quality for lossy formats (1-100, default: 80)crop_image
Extract a specific region from an image.
Parameters:
- inputPath: Path to input image
- outputPath: Path to save cropped image
- left: Left offset in pixels
- top: Top offset in pixels
- width: Width of crop area
- height: Height of crop areacompress_image
Reduce image file size with quality settings.
Parameters:
- inputPath: Path to input image
- outputPath: Path to save compressed image
- quality: Compression quality (1-100, default: 80)
- progressive: Use progressive encoding for JPEG (default: true)rotate_image
Rotate an image by any angle.
Parameters:
- inputPath: Path to input image
- outputPath: Path to save rotated image
- angle: Rotation angle in degrees (positive = clockwise)
- background: Background color for exposed areas (default: #000000)flip_image
Mirror an image horizontally or vertically.
Parameters:
- inputPath: Path to input image
- outputPath: Path to save flipped image
- direction: Flip direction (horizontal, vertical, both)get_image_info
Extract metadata and information about an image.
Parameters:
- inputPath: Path to image file
Returns: format, dimensions, color space, file size, etc.batch_resize
Generate multiple sizes from one image (perfect for responsive images).
Parameters:
- inputPath: Path to input image
- outputDir: Directory to save resized images
- sizes: Array of {width, height?, suffix}
- format: Output format for all sizes (optional)Example Usage in Claude
"Please resize image.jpg to 800x600 pixels"
"Convert photo.png to WebP format with 90% quality"
"Crop avatar.jpg starting at (100, 100) with 200x200 size"
"Generate thumbnail sizes: 150px, 300px, and 600px wide"Development
# Install dependencies
npm install
# Build TypeScript
npm run build
# Run in development mode
npm run devLicense
MIT
Available Tools
8 toolsbatch_resizeC
Generate multiple sizes from one image
| Name | Required | Description | Default |
|---|---|---|---|
| inputPath | Yes | Path to input image | |
| outputDir | Yes | Directory to save resized images | |
| sizes | Yes | Array of sizes to generate | |
| format | No | Output format for all sizes |
TDQS
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. 'Generate multiple sizes' implies a write/mutation operation, but it doesn't specify whether this overwrites existing files, requires specific permissions, has rate limits, or what happens on failure. For a tool that creates multiple output files, this lack of behavioral context 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that immediately conveys the core functionality. There's zero wasted language or unnecessary elaboration. It's appropriately sized for the tool's complexity and gets straight to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 inadequate. It doesn't explain what the tool returns, how errors are handled, or the behavioral implications of generating multiple files. Given the complexity and lack of structured metadata, the description should provide more operational context to be complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 information beyond what's in the schema - it doesn't explain relationships between parameters or provide usage examples. This meets the baseline for high schema coverage but doesn't add extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate multiple sizes from one image' clearly states the verb ('generate') and resource ('multiple sizes from one image'), making the purpose immediately understandable. It distinguishes from siblings like 'resize_image' by specifying batch/multiple operations, though it doesn't explicitly contrast with all alternatives like 'compress_image' or 'crop_image'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 doesn't mention scenarios where batch resizing is preferred over single resizing ('resize_image'), or when other tools like 'compress_image' or 'convert_format' might be more appropriate. There's no context about prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compress_imageC
Compress an image with quality settings
| Name | Required | Description | Default |
|---|---|---|---|
| inputPath | Yes | Path to input image | |
| outputPath | Yes | Path to save compressed image | |
| quality | No | Compression quality (1-100) | |
| progressive | No | Use progressive encoding (for JPEG) |
TDQS
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. 'Compress' implies a mutation operation that likely reduces file size, but the description doesn't state whether this is lossy/lossless, what formats are supported, whether the original file is preserved, or what happens on failure. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's appropriately sized for a tool with good schema documentation and gets straight to the point without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is inadequate. It doesn't explain what the tool returns, what side effects occur, what error conditions might arise, or how it differs from sibling tools. The 100% schema coverage helps with parameters, but overall completeness is poor for a tool that modifies files.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 four parameters thoroughly. The description adds minimal value beyond the schema - it mentions 'quality settings' which aligns with the 'quality' parameter but doesn't provide additional context about the other parameters (inputPath, outputPath, progressive). 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Compress') and resource ('an image') with the specific operation ('with quality settings'). It distinguishes from siblings like resize_image or convert_format by focusing on compression rather than dimension/format changes. However, it doesn't explicitly differentiate from all siblings (e.g., batch_resize might also affect file size).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 doesn't mention when compression is appropriate (e.g., for reducing file size), when not to use it (e.g., for lossless operations), or which sibling tools might be alternatives for related tasks like format conversion or resizing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
convert_formatC
Convert image between formats (jpeg, png, webp, avif)
| Name | Required | Description | Default |
|---|---|---|---|
| inputPath | Yes | Path to input image | |
| outputPath | Yes | Path to save converted image | |
| format | Yes | Target format | |
| quality | No | Quality for lossy formats (1-100) |
TDQS
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 states the tool converts images between formats but lacks details on behavioral traits: it doesn't mention whether the conversion is lossy/lossless, if it overwrites existing files, what happens on errors, or performance aspects like speed. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose and lists key formats. It is front-loaded with the core action and avoids unnecessary details, making it easy to parse quickly. Every word contributes to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations, no output schema, and involves mutation (converting images), the description is incomplete. It doesn't cover return values, error handling, or important behavioral aspects like file overwriting. For a 4-parameter tool with siblings, more context is needed to ensure safe and correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 (inputPath, outputPath, format, quality) with descriptions and constraints. The description adds minimal value beyond the schema by listing some formats, but it doesn't explain parameter interactions or provide additional context like default behaviors. 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'convert' and resource 'image', specifying the action and target. It lists supported formats (jpeg, png, webp, avif), which helps distinguish it from siblings like resize or crop. However, it doesn't explicitly differentiate from format-related siblings like compress_image, which might also handle format changes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 doesn't mention siblings like compress_image (which might involve format conversion) or get_image_info (for checking current format), nor does it specify prerequisites such as needing valid image files. Usage is implied by the action but not explicitly defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
crop_imageC
Crop an image to specified region
| Name | Required | Description | Default |
|---|---|---|---|
| inputPath | Yes | Path to input image | |
| outputPath | Yes | Path to save cropped image | |
| left | Yes | Left offset in pixels | |
| top | Yes | Top offset in pixels | |
| width | Yes | Width of crop area | |
| height | Yes | Height of crop area |
TDQS
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 states the action 'crop an image' but doesn't mention whether this is a destructive operation (overwrites the original?), requires specific file permissions, handles errors (e.g., invalid paths), or has performance considerations. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded with the core action, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an image manipulation tool with no annotations and no output schema, the description is inadequate. It doesn't explain what the tool returns (e.g., success status, error messages), behavioral traits like file handling or error conditions, or usage context relative to siblings. This leaves the agent with insufficient information for reliable invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 six parameters (inputPath, outputPath, left, top, width, height) with clear descriptions. The description adds no additional meaning beyond implying cropping uses a region defined by these parameters, which is already evident from the schema. 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('crop') and resource ('image') with the specific action 'to specified region'. It distinguishes from siblings like resize_image or rotate_image by focusing on cropping rather than resizing or rotating. However, it doesn't explicitly differentiate from batch_resize which handles multiple images, leaving some ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 resize_image (for changing dimensions) or batch_resize (for processing multiple images). There's no mention of prerequisites, such as needing an existing image file, or exclusions, like not handling non-image files.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
flip_imageB
Flip an image horizontally or vertically
| Name | Required | Description | Default |
|---|---|---|---|
| inputPath | Yes | Path to input image | |
| outputPath | Yes | Path to save flipped image | |
| direction | Yes | Flip direction |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the transformation but doesn't disclose behavioral traits like whether it modifies the original file, requires specific permissions, handles errors, supports image formats, or has performance implications. 'Flip an image' implies mutation but lacks details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is minimally complete for a simple transformation tool. It covers the basic purpose but lacks details on behavior, usage context, or output, leaving gaps that could hinder an agent's effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 three parameters thoroughly. The description adds no additional meaning beyond what the schema provides, such as explaining the 'both' direction or path requirements, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('flip') and resource ('image'), and distinguishes from siblings by specifying the transformation type (horizontal/vertical/both) rather than other operations like resize, compress, convert, crop, get info, or rotate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. While the purpose is clear, there's no mention of prerequisites, when flipping is appropriate versus other transformations, or any constraints on input/output paths.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_image_infoB
Get metadata and information about an image
| Name | Required | Description | Default |
|---|---|---|---|
| inputPath | Yes | Path to image file |
TDQS
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 states this is a 'Get' operation, implying it's likely read-only and non-destructive, but it doesn't explicitly confirm this or describe any side effects, error conditions, or output format. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It is front-loaded with the core action and resource, making it easy to parse quickly. Every part of the sentence earns its place by conveying essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read operation with one parameter and no output schema, the description is minimally adequate but lacks depth. It doesn't explain what metadata is returned (e.g., dimensions, format, size) or any constraints, which would be helpful given the absence of annotations and output schema. The context signals indicate low complexity, so the description meets basic needs but could be more informative.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'inputPath' clearly documented as 'Path to image file'. The description adds no additional meaning beyond this, such as acceptable file formats or path syntax. Given the high schema coverage, the baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('metadata and information about an image'), making the purpose immediately understandable. However, it doesn't differentiate this read operation from its siblings like 'batch_resize' or 'crop_image', which are image manipulation tools, so it doesn't fully distinguish itself in context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 doesn't mention that this is for metadata retrieval rather than image processing like the sibling tools, nor does it specify prerequisites such as needing an existing image file. Without such context, the agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
resize_imageC
Resize an image to specified dimensions
| Name | Required | Description | Default |
|---|---|---|---|
| inputPath | Yes | Path to input image | |
| outputPath | Yes | Path to save resized image | |
| width | No | Target width in pixels | |
| height | No | Target height in pixels | |
| fit | No | How the image should be resized to fit | cover |
| preserveAspectRatio | No | Maintain original aspect ratio |
TDQS
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 states the tool resizes images but doesn't mention potential side effects like overwriting output files, performance considerations for large images, or error handling for invalid inputs. This leaves significant gaps in understanding how the tool behaves beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like file handling, error cases, or output specifics, leaving the agent with insufficient context to use the tool effectively beyond basic invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 6 parameters. The description adds no additional meaning beyond what's in the schema, such as explaining the practical use of 'fit' options or 'preserveAspectRatio'. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Resize') and resource ('an image') with the specific purpose of adjusting dimensions. It distinguishes from siblings like 'crop_image' or 'rotate_image' by focusing on resizing, though it doesn't explicitly differentiate from 'batch_resize' which handles multiple images.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 'batch_resize' for multiple images or 'compress_image' for size reduction. The description lacks context about prerequisites, such as needing valid image paths, or exclusions for when other tools might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rotate_imageC
Rotate an image by specified degrees
| Name | Required | Description | Default |
|---|---|---|---|
| inputPath | Yes | Path to input image | |
| outputPath | Yes | Path to save rotated image | |
| angle | Yes | Rotation angle in degrees (positive = clockwise) | |
| background | No | Background color for exposed areas (hex or named color) | #000000 |
TDQS
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 the rotation action but fails to describe key behaviors: whether the original file is modified or a new file is created, what happens if paths are invalid, if there are size/format limitations, or what the output looks like. This leaves significant gaps 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it easy to scan and understand immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain the behavioral implications (e.g., file creation vs. modification), error conditions, or output format. Given the complexity of image processing and lack of structured safety hints, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description adds no additional semantic context beyond implying rotation, which the schema's 'angle' parameter already covers. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('rotate') and resource ('an image'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'flip_image' or 'crop_image' which also transform images, missing an opportunity to clarify when rotation is specifically needed versus other transformations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 'flip_image' or 'crop_image', nor does it mention prerequisites such as needing valid image files. It simply states what the tool does without contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
8 tool updates
- First observed
batch_resize - First observed
compress_image - First observed
convert_format - First observed
crop_image - First observed
flip_image - First observed
get_image_info - First observed
resize_image - First observed
rotate_image
TDQS
Each tool has a clearly distinct purpose targeting specific image manipulation operations. There is no overlap or ambiguity between tools like resize_image vs. batch_resize (single vs. multiple), compress_image vs. convert_format (quality vs. format), or crop vs. flip vs. rotate.
All tools follow a consistent verb_noun pattern with snake_case naming. The verbs are precise and descriptive (batch_resize, compress_image, convert_format, crop_image, flip_image, get_image_info, resize_image, rotate_image), creating a predictable and readable set.
With 8 tools, this is well-scoped for an image processing server. Each tool earns its place by covering essential operations like resizing, compression, conversion, cropping, flipping, metadata retrieval, and rotation without being overwhelming or insufficient.
The toolset provides comprehensive coverage for core image manipulation tasks, including format conversion, resizing, cropping, rotation, flipping, compression, and metadata. A minor gap might be the lack of advanced operations like filters or effects, but the basics are fully covered for practical use.
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- AlicenseAqualityDmaintenanceEnables conversion between multiple image formats including JPG, PNG, WebP, GIF, BMP, TIFF, SVG, ICO, and AVIF with quality control and batch processing capabilities.4982MIT
- FlicenseNot gradedqualityDmaintenanceEnables image and video processing through FFmpeg, including compression, format conversion, resizing, and batch processing operations for common media formats.-
- AlicenseNot gradedqualityDmaintenanceEnables comprehensive image processing, stock image search from Pexels and Pixabay, and AI image generation using OpenAI DALL-E with support for resizing, format conversion, color extraction, and watermarking.17MIT
- AlicenseAqualityBmaintenanceProvides 33 image processing tools ranging from local edits like resizing and cropping to AI-powered features like background removal and upscaling. It also supports advanced professional workflows for generating AI product photography and virtual try-on scenes.3221MIT
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