이미지 다운로드 및 최적화 도구를 제공하는 MCP 서버입니다. 모델 컨텍스트 프로토콜(MCP)을 사용하여 구축된 이 서버를 통해 AI 비서가 URL에서 이미지를 다운로드하고 기본적인 이미지 최적화 작업을 수행할 수 있습니다.
적절한 오류 처리를 통해 URL에서 이미지 다운로드
다음 옵션을 사용하여 이미지를 최적화하세요.
크기 조정(종횡비 유지)
품질 조정(JPEG/WebP)
형식 변환
Related MCP server: img-src MCP Server
지엑스피1
MCP 구성에 서버를 추가합니다(예: Claude Desktop의 구성):
{
"mcpServers": {
"image-downloader": {
"command": "node",
"args": ["/path/to/mcp-image-downloader/build/index.js"]
}
}
}URL에서 지정된 경로로 이미지를 다운로드합니다.
매개변수:
url : 다운로드할 이미지의 URL
outputPath : 이미지를 저장할 경로
이미지의 최적화된 버전을 만듭니다.
매개변수:
inputPath : 입력 이미지 경로
outputPath : 최적화된 이미지를 저장할 경로
width (선택 사항): 대상 너비(너비만 지정된 경우 종횡비 유지)
height (선택 사항): 대상 높이(높이만 지정된 경우 종횡비 유지)
quality (선택 사항): JPEG/WebP 품질(1-100)
# Run in development mode
npm run start
# Build the project
npm run buildNode.js 16 이상
NPM 또는 호환 패키지 관리자
MIT 라이센스 - 자세한 내용은 LICENSE 파일을 참조하세요.
qpd-v
0.1.0 - 최초 릴리스
Download an image from a URL to a specified path
| Name | Required | Description | Default |
|---|---|---|---|
| outputPath | Yes | Path where to save the image | |
| url | Yes | URL of the image to download |
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 to a path but doesn't cover critical aspects like error handling (e.g., invalid URLs, network failures), file overwriting behavior, supported image formats, or authentication needs. This leaves significant gaps for an agent to understand how the tool behaves in practice.
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 function without unnecessary words. It is front-loaded and wastes no space, making it easy for an agent to parse quickly.
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 a download operation (which involves network I/O and file system changes), the description is insufficient. With no annotations, no output schema, and minimal behavioral details, it fails to provide enough context for safe and effective use. Key aspects like error conditions, performance implications, or return values are missing.
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, clearly documenting both parameters ('url' and 'outputPath'). The description adds minimal value beyond this, only reiterating that the URL is for the image and the path is for saving it. 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 action ('download') and resource ('image from a URL'), making the purpose immediately understandable. However, it doesn't differentiate from the sibling tool 'optimize_image' (which likely processes images rather than downloading them), so it doesn't reach the highest score.
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 'optimize_image' or other potential tools. It states what the tool does but offers no context about appropriate use cases, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create an optimized version of an image
| Name | Required | Description | Default |
|---|---|---|---|
| height | No | Target height (maintains aspect ratio if only height is specified) | |
| inputPath | Yes | Path to the input image | |
| outputPath | Yes | Path where to save the optimized image | |
| quality | No | JPEG/WebP quality (1-100) | |
| width | No | Target width (maintains aspect ratio if only width is specified) |
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 the basic action without disclosing behavioral traits like performance characteristics, error conditions, or side effects. It mentions optimization but doesn't explain what that entails (e.g., compression, format changes).
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 purpose 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?
For a mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'optimized' means, what formats are supported, or what happens on failure. The context signals indicate moderate complexity (5 parameters) that warrants more explanation.
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 semantics beyond what's in the schema, 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 action ('Create an optimized version') and resource ('of an image'), making the purpose immediately understandable. It distinguishes from the sibling 'download_image' by focusing on transformation rather than retrieval, though it doesn't explicitly contrast them.
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, nor does it mention prerequisites or constraints. It simply states what the tool does without context about appropriate scenarios or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0download_imageoptimize_imageThe two tools have clearly distinct purposes: one downloads images from external sources, while the other processes existing images for optimization. There is no overlap in functionality, making it impossible to confuse them.
Both tools follow a consistent verb_noun pattern (download_image and optimize_image) with clear, descriptive names. The naming convention is uniform and predictable throughout the set.
With only two tools, the server feels under-scoped for an image downloader domain. Key operations like listing available images, handling errors, or supporting different image formats are missing, making the toolset too minimal for robust agent workflows.
The toolset is severely incomplete for an image downloader domain. It lacks basic CRUD operations such as listing or deleting images, error handling tools, and support for varied input sources or formats, which will likely cause agent failures in practical use.
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