{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_antivirusakash-blog-imagegen-mcps","slug":"antivirusakash-blog-imagegen-mcps","name":"OpenAI Image Generator","type":"mcp","url":"https://smithery.ai/servers/antivirusakash/blog-imagegen-mcps","page_url":"https://unfragile.ai/antivirusakash-blog-imagegen-mcps","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","openai","image-generation","smithery:antivirusakash/blog-imagegen-mcps"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_antivirusakash-blog-imagegen-mcps__cap_0","uri":"capability://tool.use.integration.mcp.standardized.image.generation.via.openai.dall.e","name":"mcp-standardized image generation via openai dall-e","description":"Exposes OpenAI's image generation models (DALL-E 3 via gpt-image-1) through the Model Context Protocol (MCP) server interface, enabling any MCP-compatible client to invoke image generation without direct API integration. The server translates MCP tool-call requests into OpenAI API calls, handles authentication via environment variables, and returns image URLs and metadata back through the MCP protocol layer.","intents":["I want to add image generation to my Claude desktop app or MCP-aware IDE without writing OpenAI SDK code","I need to expose image generation as a standardized tool that multiple AI agents can call","I want to build a workflow where Claude can generate images on-demand as part of a larger task"],"best_for":["MCP client developers (Claude Desktop, Cline, other MCP-aware tools)","Teams building multi-agent systems that need standardized image generation","Developers wanting to avoid direct OpenAI SDK dependency management"],"limitations":["Requires OpenAI API key with DALL-E 3 access — no free tier support","Image generation latency is inherited from OpenAI (typically 10-60 seconds per image)","No built-in image caching or deduplication — each request generates a new image","MCP protocol overhead adds ~100-200ms per request for serialization/deserialization","No support for image editing or variation endpoints — generation only"],"requires":["OpenAI API key with DALL-E 3 access","MCP-compatible client (Claude Desktop 0.1.0+, Cline, or custom MCP client)","Node.js 18+ or Python 3.9+ (depending on server implementation)","Network access to OpenAI API endpoints"],"input_types":["text (prompt string)","structured JSON (size, quality, style parameters)"],"output_types":["image URL (HTTPS)","image metadata (size, revised_prompt, created timestamp)"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antivirusakash-blog-imagegen-mcps__cap_1","uri":"capability://image.visual.prompt.to.image.generation.with.parameter.control","name":"prompt-to-image generation with parameter control","description":"Accepts natural language prompts and optional generation parameters (image size, quality level, style) and translates them into OpenAI DALL-E 3 API calls. The server validates prompt length and parameter ranges, constructs the API request payload, and returns the generated image URL along with the revised prompt that DALL-E actually used for generation.","intents":["I want to generate an image from a text description without manually calling OpenAI's API","I need to control image size and quality settings programmatically","I want to see what prompt DALL-E actually used after revision for transparency"],"best_for":["Content creators building AI-assisted workflows","Developers prototyping image-generation features in larger applications","Non-technical users accessing image generation through Claude or other MCP clients"],"limitations":["DALL-E 3 automatically revises prompts for safety — original prompt may not match output","Limited to DALL-E 3 model only — no support for DALL-E 2 or other image models","Image size restricted to OpenAI's supported dimensions (1024x1024, 1024x1792, 1792x1024)","Quality parameter only supports 'standard' and 'hd' — no fine-grained control","No batch generation — each prompt requires a separate API call and incurs separate costs"],"requires":["OpenAI API key with DALL-E 3 access","Valid text prompt (1-4000 characters recommended)","Optional: size parameter (1024x1024, 1024x1792, or 1792x1024)","Optional: quality parameter ('standard' or 'hd')"],"input_types":["text (natural language prompt)"],"output_types":["image URL (HTTPS, expires after 1 hour)","revised_prompt (string showing DALL-E's safety-adjusted version)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antivirusakash-blog-imagegen-mcps__cap_2","uri":"capability://tool.use.integration.mcp.tool.registration.and.schema.definition","name":"mcp tool registration and schema definition","description":"Registers image generation as a callable tool within the MCP protocol by defining a JSON schema that describes input parameters (prompt, size, quality), output format, and tool metadata. The server exposes this schema to MCP clients during the initialization handshake, allowing clients like Claude to discover the tool and construct valid requests without hardcoding implementation details.","intents":["I want Claude or another MCP client to automatically discover that image generation is available","I need to ensure my MCP client sends properly formatted image generation requests","I want to document the image generation API contract in a standardized way"],"best_for":["MCP server developers building tool integrations","Teams standardizing on MCP for multi-tool orchestration","Developers building custom MCP clients that need tool discovery"],"limitations":["Schema definition is static — cannot dynamically adjust based on OpenAI API changes without server restart","MCP clients must implement schema validation themselves — server does minimal validation","No versioning mechanism for tool schema — breaking changes require client updates","Schema complexity limited by MCP's JSON schema subset — cannot express all OpenAI parameter constraints"],"requires":["MCP protocol 1.0+ support","JSON schema understanding in client implementation","Server restart to update tool schema"],"input_types":["JSON schema (tool definition)"],"output_types":["tool schema metadata (name, description, input_schema, output_schema)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antivirusakash-blog-imagegen-mcps__cap_3","uri":"capability://safety.moderation.openai.api.credential.management.via.environment.variables","name":"openai api credential management via environment variables","description":"Manages OpenAI API authentication by reading the OPENAI_API_KEY from environment variables at server startup, eliminating the need to pass credentials in each request. The server stores the key in memory and uses it for all subsequent API calls to OpenAI, with no credential logging or persistence to disk.","intents":["I want to securely pass my OpenAI API key to the MCP server without hardcoding it","I need the server to automatically authenticate with OpenAI on every request","I want to rotate API keys without redeploying the server"],"best_for":["Developers deploying MCP servers in containerized or cloud environments","Teams following 12-factor app principles for credential management","Users running the server locally with environment-based configuration"],"limitations":["Credentials stored in memory — no encryption at rest, vulnerable if process is dumped","No credential rotation without server restart","Environment variable approach assumes secure host environment — not suitable for untrusted execution contexts","No audit logging of API key usage — cannot track which requests used which key","Single API key only — no support for multiple accounts or key rotation strategies"],"requires":["OPENAI_API_KEY environment variable set before server startup","Valid OpenAI API key with DALL-E 3 access"],"input_types":["environment variable (string)"],"output_types":["authenticated HTTP headers (Authorization: Bearer)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antivirusakash-blog-imagegen-mcps__cap_4","uri":"capability://data.processing.analysis.http.response.handling.and.image.url.extraction","name":"http response handling and image url extraction","description":"Parses OpenAI's image generation API responses (JSON with nested image objects), extracts the image URL and metadata, and formats them into MCP-compatible output. Handles HTTP status codes, error responses, and timeout scenarios, returning structured error messages to the MCP client for debugging.","intents":["I want to reliably extract the generated image URL from OpenAI's response","I need clear error messages when image generation fails","I want to know metadata like the revised prompt and creation timestamp"],"best_for":["MCP server developers handling third-party API responses","Teams needing robust error handling in API integration layers","Developers debugging image generation failures"],"limitations":["Image URLs expire after 1 hour — no built-in persistence or download mechanism","Error messages depend on OpenAI's response format — format changes break parsing","No retry logic for transient failures — client must implement retries","Response parsing assumes OpenAI API contract — no fallback for API changes","No streaming support — entire response must be buffered before parsing"],"requires":["HTTP client library (Node.js fetch, Python requests, etc.)","JSON parsing capability","Timeout handling (recommended: 120 seconds for image generation)"],"input_types":["HTTP response (JSON)"],"output_types":["structured JSON (image_url, revised_prompt, created, size)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antivirusakash-blog-imagegen-mcps__cap_5","uri":"capability://tool.use.integration.client.agnostic.image.generation.invocation.via.mcp.protocol","name":"client-agnostic image generation invocation via mcp protocol","description":"Implements the MCP tool-calling protocol to receive image generation requests from any MCP-compatible client (Claude Desktop, Cline, custom agents), parse the tool-call message, validate parameters, and return results in MCP's standardized response format. 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