{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_antivirusakash-open-imagegen-mcp","slug":"antivirusakash-open-imagegen-mcp","name":"OpenAI Image Generator","type":"mcp","url":"https://smithery.ai/servers/antivirusakash/open-imagegen-mcp","page_url":"https://unfragile.ai/antivirusakash-open-imagegen-mcp","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","openai","image-generation","smithery:antivirusakash/open-imagegen-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_antivirusakash-open-imagegen-mcp__cap_0","uri":"capability://image.visual.mcp.standardized.image.generation.via.openai.dall.e.3","name":"mcp-standardized image generation via openai dall-e 3","description":"Exposes OpenAI's gpt-image-1 (DALL-E 3) model through the Model Context Protocol (MCP) server interface, enabling any MCP-compatible client to invoke image generation without direct API integration. The server handles authentication via OpenAI API keys, marshals text prompts into OpenAI's image generation endpoint, and returns image URLs with metadata. Uses MCP's tool-calling schema to define image generation as a callable resource with standardized request/response serialization.","intents":["I want to add image generation to my Claude desktop app or IDE without writing OpenAI API code","I need to call DALL-E 3 from a multi-tool agent that already speaks MCP","I'm building a workflow tool and want image generation as a pluggable capability"],"best_for":["MCP client developers (Claude Desktop, Cline, custom agents) integrating image generation","Teams building agent frameworks that need standardized tool interfaces","Non-engineers using MCP-compatible applications who want DALL-E access without CLI setup"],"limitations":["Requires valid OpenAI API key with DALL-E 3 access and active billing — no free tier support","No image editing or variation capabilities — only text-to-image generation","Inherits OpenAI's rate limits and content policy restrictions (no explicit filtering at MCP layer)","Returns only image URLs; does not persist, cache, or batch-process images locally","No support for advanced DALL-E parameters like style, quality, or size variants if not exposed in MCP schema"],"requires":["OpenAI API key with DALL-E 3 access","MCP-compatible client (Claude Desktop, Cline, or custom MCP host)","Network connectivity to OpenAI API endpoints","Python 3.8+ or Node.js 16+ (depending on server implementation)"],"input_types":["text (natural language image prompt, 1-4000 characters)"],"output_types":["image URL (HTTPS link to generated image)","structured metadata (prompt echo, generation timestamp, image dimensions if available)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antivirusakash-open-imagegen-mcp__cap_1","uri":"capability://image.visual.prompt.to.image.generation.with.openai.authentication.abstraction","name":"prompt-to-image generation with openai authentication abstraction","description":"Handles end-to-end credential management and API communication with OpenAI's image generation endpoint. The server accepts a text prompt from the MCP client, authenticates using an OpenAI API key (loaded from environment or config), constructs a properly formatted request to OpenAI's image generation API, and returns the generated image URL. Abstracts away HTTP request construction, error handling, and API versioning details from the client.","intents":["I want to generate an image from a text description without managing OpenAI credentials in my application","I need a simple, stateless image generation service that handles API errors gracefully","I'm integrating image generation into a multi-step workflow and want a single, reliable endpoint"],"best_for":["Application developers who want to delegate OpenAI API management to a separate service","Teams building agent systems where image generation is one of many tools","Developers avoiding direct dependency on OpenAI SDK in their main application"],"limitations":["Single-threaded or limited concurrency — may queue requests if multiple clients call simultaneously","No built-in retry logic or exponential backoff for rate-limited or transient OpenAI API failures","Credentials stored in environment variables — no secrets management integration (Vault, AWS Secrets Manager)","No request validation — malformed or adversarial prompts passed directly to OpenAI, relying on their content policy","Synchronous request/response model — long image generation times (30-60 seconds) block the MCP client"],"requires":["OpenAI API key with DALL-E 3 model access","Environment variable or config file to store API key securely","Outbound HTTPS connectivity to api.openai.com"],"input_types":["text (image prompt string)"],"output_types":["image URL (string)","error message (if API call fails)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antivirusakash-open-imagegen-mcp__cap_2","uri":"capability://data.processing.analysis.text.prompt.validation.and.transformation.for.image.generation","name":"text prompt validation and transformation for image generation","description":"Processes incoming text prompts before sending them to OpenAI's image generation API. May include prompt enhancement (e.g., adding style descriptors), length validation (ensuring prompts fit OpenAI's limits), sanitization of special characters, or logging for audit trails. The server applies these transformations transparently, allowing clients to send raw prompts while the server optimizes them for the underlying model.","intents":["I want to ensure user prompts are within OpenAI's character limits before API calls","I need to log all image generation requests for compliance or debugging","I want to automatically enhance vague prompts with style or quality hints"],"best_for":["Production deployments requiring audit trails or compliance logging","Teams wanting to improve image quality through prompt engineering without client-side logic","Applications with user-generated prompts that need validation before API submission"],"limitations":["Prompt transformation logic is opaque to clients — they cannot see what prompt was actually sent to OpenAI","No machine learning-based prompt optimization — only rule-based transformations","Validation rules are static and cannot be updated without restarting the MCP server","No A/B testing framework to compare original vs. enhanced prompts"],"requires":["MCP server implementation with prompt processing hooks","Optional: logging infrastructure (file, database, or observability platform)"],"input_types":["text (raw image prompt)"],"output_types":["text (validated/transformed prompt)","validation status (pass/fail with reason)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antivirusakash-open-imagegen-mcp__cap_3","uri":"capability://memory.knowledge.image.generation.result.caching.and.deduplication","name":"image generation result caching and deduplication","description":"Caches generated images based on prompt hashes, returning cached results for duplicate or similar prompts without re-invoking the OpenAI API. Uses a local cache store (in-memory, Redis, or file-based) keyed by prompt hash or semantic similarity. When a client requests an image for a prompt that has been recently generated, the server returns the cached URL and metadata instead of making a new API call, reducing latency and API costs.","intents":["I want to reduce OpenAI API costs by avoiding duplicate image generations for the same prompt","I need faster image generation responses for frequently-requested images","I'm building a demo or prototype and want to minimize API calls during testing"],"best_for":["Production systems with high request volume and repeated prompts","Cost-sensitive deployments where API spend is a constraint","Development and testing environments where rapid iteration is needed"],"limitations":["Cache invalidation is manual or time-based — no automatic refresh when OpenAI's model improves","Semantic similarity matching is not implemented — only exact prompt hash matching (no fuzzy matching)","Cache storage adds operational overhead — requires persistent storage (Redis, database) for multi-instance deployments","Cached images may become stale or broken if OpenAI deletes URLs after a retention period","No cache warming or precomputation — cache only grows reactively as requests arrive"],"requires":["Local cache store (in-memory for single-instance, Redis/database for distributed)","Cache key generation logic (prompt hashing)","Optional: cache TTL configuration and eviction policy"],"input_types":["text (image prompt)"],"output_types":["image URL (from cache or fresh generation)","cache hit/miss metadata"],"categories":["memory-knowledge","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antivirusakash-open-imagegen-mcp__cap_4","uri":"capability://safety.moderation.error.handling.and.graceful.degradation.for.api.failures","name":"error handling and graceful degradation for api failures","description":"Implements error handling for OpenAI API failures (rate limits, authentication errors, service outages, network timeouts). The server catches exceptions from the OpenAI API, maps them to meaningful error messages, and returns them to the MCP client with appropriate HTTP status codes or MCP error responses. May include retry logic with exponential backoff, fallback to cached results, or graceful error messages that guide users to resolve issues.","intents":["I want my application to handle OpenAI API rate limits without crashing","I need clear error messages when image generation fails so I can debug or retry","I want automatic retries for transient failures without manual intervention"],"best_for":["Production deployments requiring high availability and resilience","Applications with unpredictable or bursty image generation demand","Teams without deep OpenAI API expertise who need clear error guidance"],"limitations":["Retry logic may be simplistic — no adaptive backoff based on error type or remaining quota","No circuit breaker pattern — repeated failures may continue to hammer the API","Error messages may leak internal details (API keys, request IDs) if not carefully sanitized","Fallback strategies (e.g., returning cached images) may not be appropriate for all use cases","No monitoring or alerting integration — errors are logged but not automatically escalated"],"requires":["Error handling framework in the MCP server implementation","Optional: retry library (e.g., tenacity, exponential-backoff)","Optional: monitoring/alerting integration (Sentry, DataDog, CloudWatch)"],"input_types":["API error responses from OpenAI"],"output_types":["error message (human-readable or structured)","retry status (will retry, permanent failure, etc.)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antivirusakash-open-imagegen-mcp__cap_5","uri":"capability://tool.use.integration.mcp.tool.schema.definition.and.discovery","name":"mcp tool schema definition and discovery","description":"Defines the image generation capability as a standardized MCP tool with a JSON schema that describes input parameters (prompt text), output types (image URL), and metadata (description, examples). MCP clients discover this schema via the server's tool listing endpoint, allowing them to understand what parameters are required, validate inputs before sending, and render UI elements (forms, buttons) for invoking the tool. The schema follows MCP's tool definition standard, enabling interoperability with any MCP-compatible client.","intents":["I want my MCP client to automatically discover and display available image generation options","I need to validate user inputs against the tool schema before sending requests","I'm building a generic MCP client and want to render UI for any tool without hardcoding"],"best_for":["MCP client developers building generic tool discovery and invocation UIs","Framework developers creating MCP-compatible applications","Teams standardizing on MCP for multi-tool agent systems"],"limitations":["Schema is static — cannot be updated dynamically based on OpenAI's API changes","No schema versioning — clients cannot handle multiple versions of the tool schema","Schema validation is client-side responsibility — server does not enforce schema compliance","No support for conditional parameters or dynamic schema generation based on runtime state","Limited to MCP's schema format — cannot express complex constraints or dependencies"],"requires":["MCP server implementation with tool schema support","JSON schema definition for image generation parameters","MCP-compatible client with tool discovery support"],"input_types":["tool discovery request (MCP protocol)"],"output_types":["JSON schema (tool definition with parameters, description, examples)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with DALL-E 3 access","MCP-compatible client (Claude Desktop, Cline, or custom MCP host)","Network connectivity to OpenAI API endpoints","Python 3.8+ or Node.js 16+ (depending on server implementation)","OpenAI API key with DALL-E 3 model access","Environment variable or config file to store API key securely","Outbound HTTPS connectivity to api.openai.com","MCP server implementation with prompt processing hooks","Optional: logging infrastructure (file, database, or observability platform)","Local cache store (in-memory for single-instance, Redis/database for distributed)"],"failure_modes":["Requires valid OpenAI API key with DALL-E 3 access and active billing — no free tier support","No image editing or variation capabilities — only text-to-image generation","Inherits OpenAI's rate limits and content policy restrictions (no explicit filtering at MCP layer)","Returns only image URLs; does not persist, cache, or batch-process images locally","No support for advanced DALL-E parameters like style, quality, or size variants if not exposed in MCP schema","Single-threaded or limited concurrency — may queue requests if multiple clients call simultaneously","No built-in retry logic or exponential backoff for rate-limited or transient OpenAI API failures","Credentials stored in environment variables — no secrets management integration (Vault, AWS Secrets Manager)","No request validation — malformed or adversarial prompts passed directly to OpenAI, relying on their content policy","Synchronous request/response model — long image generation times (30-60 seconds) block the MCP client","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.45,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.635Z","last_scraped_at":"2026-05-03T15:19:36.245Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=antivirusakash-open-imagegen-mcp","compare_url":"https://unfragile.ai/compare?artifact=antivirusakash-open-imagegen-mcp"}},"signature":"IRyL2VRLUCBIg23/Kv7BT65Mo0GxoIv0ZPOIlbJ3y0kQ0oxRNoVKt0ZeMuIVBq+4ZnRGkkJqCKexNSkV2NUSAQ==","signedAt":"2026-06-21T00:22:56.484Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/antivirusakash-open-imagegen-mcp","artifact":"https://unfragile.ai/antivirusakash-open-imagegen-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=antivirusakash-open-imagegen-mcp","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}