image-tools-mcp
Bildwerkzeuge MCP
Ein Model Context Protocol (MCP)-Dienst zum Abrufen von Bildabmessungen und Komprimieren von Bildern, der sowohl URL- als auch lokale Dateiquellen unterstützt.
Merkmale
Abrufen der Bildabmessungen aus URLs
Bildabmessungen aus lokalen Dateien abrufen
Komprimieren Sie Bilder von URLs mithilfe der TinyPNG-API
Komprimieren Sie lokale Bilder mit der TinyPNG-API
Konvertieren Sie Bilder in verschiedene Formate (webp, jpeg/jpg, png)
Gibt Informationen zu Breite, Höhe, Typ, MIME-Typ und Komprimierung zurück
Beispielergebnisse


von der Figma-URL herunterladen und komprimieren
Related MCP server: test-1
Verwendung
Verwendung als MCP-Dienst
Dieser Dienst bietet fünf Toolfunktionen:
get_image_size- Abmessungen von Remote-Bildern abrufenget_local_image_size- Abmessungen lokaler Bilder abrufencompress_image_from_url– Komprimieren Sie Remote-Bilder mit der TinyPNG-APIcompress_local_image– Komprimieren Sie lokale Bilder mit der TinyPNG-APIfigma– Bildlinks von der Figma-API abrufen und mit der TinyPNG-API komprimieren
Client-Integration
Um diesen MCP-Dienst nutzen zu können, müssen Sie sich von einem MCP-Client aus damit verbinden. Hier sind Beispiele für die Integration mit verschiedenen Clients:
Verwendung mit Claude Desktop
Installieren Sie Claude Desktop von claude.ai/download
Holen Sie sich den TinyPNG-API-Schlüssel: Besuchen Sie TinyPNG und holen Sie sich Ihren API-Schlüssel
Konfigurieren Sie Claude Desktop für die Verwendung dieses MCP-Servers, indem Sie die Konfigurationsdatei bearbeiten:
{
"mcpServers": {
"image-tools": {
"command": "npx",
"args": ["image-tools-mcp"],
"env": {
"TINIFY_API_KEY": "<YOUR_TINIFY_API_KEY>",
"FIGMA_API_TOKEN": "<YOUR_FIGMA_API_TOKEN>"
}
}
}
}Starten Sie Claude Desktop neu
Bitten Sie Claude, die Bildabmessungen zu ermitteln: „Können Sie mir die Abmessungen dieses Bildes nennen: https://example.com/image.jpg “
Bitten Sie Claude, ein Bild zu komprimieren: „Können Sie dieses Bild komprimieren: https://example.com/image.jpg “
Bitten Sie Claude, ein lokales Bild zu komprimieren: „Können Sie dieses Bild komprimieren: D:/Pfad/zu/Bild.png“
Bitten Sie Claude, einen lokalen Bildordner zu komprimieren: „Können Sie diesen Ordner komprimieren: D:/imageFolder“
Bitten Sie Claude, Bildlinks von der Figma-API abzurufen: „Können Sie Bildlinks von der Figma-API abrufen: https://www.figma.com/file/XXXXXXX “
Verwendung mit der MCP-Clientbibliothek
import { McpClient } from "@modelcontextprotocol/client";
// Initialize the client
const client = new McpClient({
transport: "stdio" // or other transport options
});
// Connect to the server
await client.connect();
// Get image dimensions from URL
const urlResult = await client.callTool("get_image_size", {
options: {
imageUrl: "https://example.com/image.jpg"
}
});
console.log(JSON.parse(urlResult.content[0].text));
// Output: { width: 800, height: 600, type: "jpg", mime: "image/jpeg" }
// Get image dimensions from local file
const localResult = await client.callTool("get_local_image_size", {
options: {
imagePath: "D:/path/to/image.png"
}
});
console.log(JSON.parse(localResult.content[0].text));
// Output: { width: 1024, height: 768, type: "png", mime: "image/png", path: "D:/path/to/image.png" }
// Compress image from URL
const compressUrlResult = await client.callTool("compress_image_from_url", {
options: {
imageUrl: "https://example.com/image.jpg",
outputFormat: "webp" // Optional: convert to webp, jpeg/jpg, or png
}
});
console.log(JSON.parse(compressUrlResult.content[0].text));
// Output: { originalSize: 102400, compressedSize: 51200, compressionRatio: "50.00%", tempFilePath: "/tmp/compressed_1615456789.webp", format: "webp" }
// Compress local image
const compressLocalResult = await client.callTool("compress_local_image", {
options: {
imagePath: "D:/path/to/image.png",
outputPath: "D:/path/to/compressed.webp", // Optional
outputFormat: "image/webp" // Optional: convert to image/webp, image/jpeg, or image/png
}
});
console.log(JSON.parse(compressLocalResult.content[0].text));
// Output: { originalSize: 102400, compressedSize: 51200, compressionRatio: "50.00%", outputPath: "D:/path/to/compressed.webp", format: "webp" }
// Fetch image links from Figma API
const figmaResult = await client.callTool("figma", {
options: {
figmaUrl: "https://www.figma.com/file/XXXXXXX"
}
});
console.log(JSON.parse(figmaResult.content[0].text));
// Output: { imageLinks: ["https://example.com/image1.jpg", "https://example.com/image2.jpg"] }
### Tool Schemas
#### get_image_size
```typescript
{
options: {
imageUrl: string // URL of the image to retrieve dimensions for
}
}get_local_image_size
{
options: {
imagePath: string; // Absolute path to the local image file
}
}Bild von URL komprimieren
{
options: {
imageUrl: string // URL of the image to compress
outputFormat?: "image/webp" | "image/jpeg" | "image/jpg" | "image/png" // Optional output format
}
}komprimieren_lokales_Bild
{
options: {
imagePath: string // Absolute path to the local image file
outputPath?: string // Optional absolute path for the compressed output image
outputFormat?: "image/webp" | "image/jpeg" | "image/jpg" | "image/png" // Optional output format
}
}figma
{
options: {
figmaUrl: string; // URL of the Figma file to fetch image links from
}
}Änderungsprotokoll
12.05.2025: Figma-API aktualisiert, um zusätzliche Parameter zu unterstützen, einschließlich 2-facher Bildskalierung.
Technische Umsetzung
Dieses Projekt basiert auf den folgenden Bibliotheken:
probe-image-size - Zur Erkennung der Bildabmessungen
tinify – Zur Bildkomprimierung über die TinyPNG-API
figma-api – Zum Abrufen von Bildlinks von der Figma-API
Umgebungsvariablen
TINIFY_API_KEY– Erforderlich für die Bildkomprimierung. Holen Sie sich Ihren API-Schlüssel von TinyPNG.Wenn nicht angegeben, werden die Komprimierungstools (
compress_image_from_urlundcompress_local_image) nicht registriert
FIGMA_API_TOKEN– Erforderlich zum Abrufen von Bildlinks von der Figma-API. Holen Sie sich Ihr API-Token von FigmaWenn nicht angegeben, wird das Figma-Tool (
figma) nicht registriert
Hinweis: Die grundlegenden Bilddimensionstools ( get_image_size und get_local_image_size ) sind unabhängig von den API-Schlüsseln immer verfügbar.
Lizenz
MIT
Available Tools
2 toolsget_image_sizeC
Get the size of an image from URL
| Name | Required | Description | Default |
|---|---|---|---|
| options | Yes | Options for retrieving image size |
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 what the tool does but lacks details on performance (e.g., network timeouts, rate limits), error handling (e.g., invalid URLs, unsupported formats), or output format (e.g., dimensions in pixels). This leaves significant gaps for a tool that performs network operations.
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, direct sentence with zero wasted words. It front-loads the core purpose ('Get the size of an image') and efficiently specifies the source ('from URL'). Every word earns its place, making it highly concise and well-structured.
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's network-based operation and lack of annotations or output schema, the description is incomplete. It doesn't address critical context like what 'size' means (e.g., dimensions, file size), potential errors, or response format. For a tool with no structured output documentation, this leaves too much unspecified.
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 the single parameter 'imageUrl'. The description adds no additional semantic context beyond implying the URL is for an image, which is already clear from the parameter name. This meets the baseline for high schema coverage but doesn't enhance understanding.
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 the size') and resource ('an image from URL'), making the purpose immediately understandable. It distinguishes from the sibling tool 'get_local_image_size' by specifying the image source as 'from URL' rather than local. However, it doesn't explicitly contrast with the sibling, so it's not a perfect 5.
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 its sibling 'get_local_image_size'. There's no mention of prerequisites, alternative scenarios, or exclusion criteria. The agent must infer usage from the name and description alone without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_local_image_sizeC
Get the size of a local image
| Name | Required | Description | Default |
|---|---|---|---|
| options | Yes | Options for retrieving local image size |
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 what the tool does but doesn't add context beyond the basic action—missing details like error handling, performance implications, or what the output looks like (e.g., dimensions in pixels). This leaves significant gaps for a tool that interacts with local files.
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, front-loading the core purpose without unnecessary details. It's appropriately sized for a simple tool, making it easy 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 lack of annotations and output schema, the description is incomplete for a tool that reads local files. It doesn't explain the return value (e.g., width and height), error cases, or security considerations, leaving the agent with insufficient context to use it effectively beyond the basic parameter.
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, documenting the 'imagePath' parameter as an absolute path. The description doesn't add any meaning beyond this, such as format examples or constraints, so it meets the baseline of 3 where the schema does the heavy lifting without extra value from the description.
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 'Get' and the resource 'size of a local image', making the purpose specific and understandable. However, it doesn't explicitly differentiate from its sibling 'get_image_size', which might handle remote images or have different scope, leaving room for ambiguity in sibling distinction.
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, such as its sibling 'get_image_size'. It lacks context on prerequisites, exclusions, or specific scenarios, offering only a basic statement of function without usage instructions.
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.
2 tool updates
v1.0.0- First observed
get_image_size - First observed
get_local_image_size
TDQS
The two tools have overlapping purposes—both retrieve image sizes—with only the source (URL vs. local) differing. This creates ambiguity as an agent might misselect between them if the input context is unclear, such as when 'image' could refer to either type. The descriptions help slightly by specifying the source, but the core functionality is identical, leading to potential confusion.
The tool names follow a consistent verb_noun pattern with 'get_image_size' and 'get_local_image_size', both using snake_case and starting with 'get'. This predictability makes it easy for an agent to understand the naming convention and infer tool purposes without deviation or mixed styles.
With only 2 tools, the server feels thin and under-scoped for an 'image-tools' domain, which typically implies a broader set of operations like resizing, converting, or analyzing images. The limited count suggests incomplete coverage, as basic image manipulation tasks beyond size retrieval are missing, making it inadequate for comprehensive image handling.
The tool set is severely incomplete for an image processing domain, covering only size retrieval from two sources. There are significant gaps in common operations such as resizing, cropping, format conversion, or metadata extraction, which will likely cause agent failures when attempting typical image-related tasks. The surface lacks core functionality needed for a coherent image tools server.
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