Image Generation MCP Server
MCP-Server zur Bildgenerierung
Ein Model Context Protocol (MCP)-Server, der die nahtlose Generierung hochwertiger Bilder mithilfe des Flux.1 Schnell-Modells über Together AI ermöglicht. Dieser Server bietet eine standardisierte Schnittstelle zur Angabe von Bildgenerierungsparametern.
Merkmale
Hochwertige Bilderzeugung durch das Flux.1 Schnell-Modell
Unterstützung für anpassbare Abmessungen (Breite und Höhe)
Klare Fehlerbehandlung für schnelle Validierung und API-Probleme
Einfache Integration mit MCP-kompatiblen Clients
Optionales Speichern des Bildes auf der Festplatte im PNG-Format
Related MCP server: Image Generation MCP Server
Installation
npm install together-mcpOder führen Sie direkt aus:
npx together-mcp@latestKonfiguration
Fügen Sie Ihrer MCP-Serverkonfiguration hinzu:
{
"mcpServers": {
"together-image-gen": {
"command": "npx",
"args": ["together-mcp@latest -y"],
"env": {
"TOGETHER_API_KEY": "<API KEY>"
}
}
}
}Verwendung
Der Server stellt ein Tool bereit: generate_image
Verwenden von generate_image
Dieses Tool verfügt nur über einen obligatorischen Parameter: die Eingabeaufforderung. Alle anderen Parameter sind optional und verwenden sinnvolle Standardwerte, falls sie nicht angegeben werden.
Parameter
{
// Required
prompt: string; // Text description of the image to generate
// Optional with defaults
model?: string; // Default: "black-forest-labs/FLUX.1-schnell-Free"
width?: number; // Default: 1024 (min: 128, max: 2048)
height?: number; // Default: 768 (min: 128, max: 2048)
steps?: number; // Default: 1 (min: 1, max: 100)
n?: number; // Default: 1 (max: 4)
response_format?: string; // Default: "b64_json" (options: ["b64_json", "url"])
image_path?: string; // Optional: Path to save the generated image as PNG
}Minimales Anforderungsbeispiel
Es ist lediglich die Eingabeaufforderung erforderlich:
{
"name": "generate_image",
"arguments": {
"prompt": "A serene mountain landscape at sunset"
}
}Vollständiges Anforderungsbeispiel mit Bildspeicherung
Überschreiben Sie alle Standardeinstellungen und geben Sie einen Pfad zum Speichern des Bilds an:
{
"name": "generate_image",
"arguments": {
"prompt": "A serene mountain landscape at sunset",
"width": 1024,
"height": 768,
"steps": 20,
"n": 1,
"response_format": "b64_json",
"model": "black-forest-labs/FLUX.1-schnell-Free",
"image_path": "/path/to/save/image.png"
}
}Antwortformat
Die Antwort ist ein JSON-Objekt mit folgendem Inhalt:
{
"id": string, // Generation ID
"model": string, // Model used
"object": "list",
"data": [
{
"timings": {
"inference": number // Time taken for inference
},
"index": number, // Image index
"b64_json": string // Base64 encoded image data (if response_format is "b64_json")
// OR
"url": string // URL to generated image (if response_format is "url")
}
]
}Wenn der Bildpfad angegeben wurde und das Speichern erfolgreich war, enthält die Antwort eine Bestätigung des Speicherorts.
Standardwerte
Wenn in der Anfrage nichts anderes angegeben ist, werden diese Standardwerte verwendet:
Modell: "black-forest-labs/FLUX.1-schnell-Free"
Breite: 1024
Höhe: 768
Schritte: 1
n: 1
Antwortformat: "b64_json"
Wichtige Hinweise
Nur der
prompt-Parameter ist erforderlichAlle optionalen Parameter verwenden Standardwerte, wenn sie nicht angegeben sind.
Wenn Parameter angegeben werden, müssen sie ihren Einschränkungen entsprechen (z. B. Breiten-/Höhenbereiche).
Base64-Antworten können groß sein. Verwenden Sie das URL-Format für größere Bilder.
Stellen Sie beim Speichern von Bildern sicher, dass das angegebene Verzeichnis vorhanden und beschreibbar ist
Voraussetzungen
Node.js >= 16
Together AI API-Schlüssel
Melden Sie sich bei api.together.xyz an
Navigieren Sie zu den API-Schlüsseleinstellungen
Klicken Sie auf „Erstellen“, um einen neuen API-Schlüssel zu generieren
Kopieren Sie den generierten Schlüssel zur Verwendung in Ihrer MCP-Konfiguration
Abhängigkeiten
{
"@modelcontextprotocol/sdk": "0.6.0",
"axios": "^1.6.7"
}Entwicklung
Klonen und erstellen Sie das Projekt:
git clone https://github.com/manascb1344/together-mcp-server
cd together-mcp-server
npm install
npm run buildVerfügbare Skripte
npm run build– Erstellen Sie das TypeScript-Projektnpm run watch- Auf Änderungen achten und neu erstellennpm run inspector– MCP-Inspektor ausführen
Beitragen
Beiträge sind willkommen! Bitte folgen Sie diesen Schritten:
Forken Sie das Repository
Erstellen Sie einen neuen Zweig (
feature/my-new-feature)Übernehmen Sie Ihre Änderungen
Schieben Sie den Zweig zu Ihrer Gabel
Öffnen einer Pull-Anfrage
Funktionsanfragen und Fehlerberichte können über GitHub Issues eingereicht werden. Bitte prüfen Sie bestehende Issues, bevor Sie ein neues erstellen.
Bei wesentlichen Änderungen öffnen Sie bitte zunächst ein Problem, um Ihre Änderungsvorschläge zu besprechen.
Lizenz
Dieses Projekt ist unter der MIT-Lizenz lizenziert. Weitere Informationen finden Sie in der Datei LICENSE.
Available Tools
1 toolgenerate_imageC
Generate an image using Together AI API
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Text prompt for image generation | |
| model | No | Model to use for generation (default: black-forest-labs/FLUX.1-schnell-Free) | |
| width | No | Image width (default: 1024) | |
| height | No | Image height (default: 768) | |
| steps | No | Number of inference steps (default: 1) | |
| n | No | Number of images to generate (default: 1) | |
| response_format | No | Response format (default: b64_json) | |
| image_path | No | Optional path to save the generated image as PNG |
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 API provider but fails to describe critical behaviors like rate limits, authentication requirements, cost implications, error handling, or what happens when saving to 'image_path'. This leaves significant gaps for a tool with 8 parameters and no output schema.
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 extremely concise with a single sentence that directly states the tool's purpose. There is zero wasted language, and it's front-loaded with the core functionality, making it highly efficient.
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 (8 parameters, no output schema, no annotations), the description is insufficient. It doesn't explain return values, error cases, or behavioral nuances, leaving the agent with incomplete information for proper tool invocation in a real-world context.
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 8 parameters. The description adds no additional parameter semantics beyond what's already in the schema, meeting the baseline score of 3 for high schema coverage without 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 clearly states the action ('Generate an image') and the target resource ('using Together AI API'), providing a specific verb+resource combination. However, with no sibling tools mentioned, there's no explicit differentiation from alternatives, preventing a perfect 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, prerequisites, or context for invocation. It simply states what the tool does without any usage instructions or exclusions.
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.
1 tool update
v0.1.7- First observed
generate_image
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose, making it impossible for an agent to misselect between multiple options.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'generate_image' follows a clear verb_noun pattern, and there are no other tools to compare it against for inconsistency.
A single tool is too few for a server named 'Image Generation MCP Server', which suggests a broader scope. While the tool covers basic generation, the lack of additional tools (e.g., for editing, listing, or managing images) makes the surface feel thin and incomplete for the implied domain.
The server is severely incomplete for an image generation domain. It only provides a generate_image tool, with no coverage for related operations like listing generated images, editing parameters, deleting images, or handling variations. This creates significant gaps that will likely cause agent failures in broader workflows.
Maintenance
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