> OpenAI Automation
Automate OpenAI API operations -- generate responses with multimodal and structured output support, create embeddings, generate images, and list models via the Composio MCP integration.
curl "https://skillshub.wtf/ComposioHQ/awesome-claude-skills/openai-automation?format=md"name: OpenAI Automation description: "Automate OpenAI API operations -- generate responses with multimodal and structured output support, create embeddings, generate images, and list models via the Composio MCP integration." requires: mcp: - rube
OpenAI Automation
Automate your OpenAI API workflows -- generate text with the Responses API (including multimodal image+text inputs and structured JSON outputs), create embeddings for search and clustering, generate images with DALL-E and GPT Image models, and list available models.
Toolkit docs: composio.dev/toolkits/openai
Setup
- Add the Composio MCP server to your client:
https://rube.app/mcp - Connect your OpenAI account when prompted (API key authentication)
- Start using the workflows below
Core Workflows
1. Generate a Response (Text, Multimodal, Structured)
Use OPENAI_CREATE_RESPONSE for one-shot model responses including text, image analysis, OCR, and structured JSON outputs.
Tool: OPENAI_CREATE_RESPONSE
Inputs:
- model: string (required) -- e.g., "gpt-5", "gpt-4o", "o3-mini"
- input: string | array (required)
Simple: "Explain quantum computing"
Multimodal: [
{ role: "user", content: [
{ type: "input_text", text: "What is in this image?" },
{ type: "input_image", image_url: { url: "https://..." } }
]}
]
- temperature: number (0-2, optional -- not supported with reasoning models)
- max_output_tokens: integer (optional)
- reasoning: { effort: "none" | "minimal" | "low" | "medium" | "high" }
- text: object (structured output config)
- format: { type: "json_schema", name: "...", schema: {...}, strict: true }
- tools: array (function, code_interpreter, file_search, web_search)
- tool_choice: "auto" | "none" | "required" | { type: "function", function: { name: "..." } }
- store: boolean (false to opt out of model distillation)
- stream: boolean
Structured output example: Set text.format to { type: "json_schema", name: "person", schema: { type: "object", properties: { name: { type: "string" }, age: { type: "integer" } }, required: ["name", "age"], additionalProperties: false }, strict: true }.
2. Create Embeddings
Use OPENAI_CREATE_EMBEDDINGS for vector search, clustering, recommendations, and RAG pipelines.
Tool: OPENAI_CREATE_EMBEDDINGS
Inputs:
- input: string | string[] | int[] | int[][] (required) -- max 8192 tokens, max 2048 items
- model: string (required) -- "text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002"
- dimensions: integer (optional, only for text-embedding-3 and later)
- encoding_format: "float" | "base64" (default "float")
- user: string (optional, end-user ID for abuse monitoring)
3. Generate Images
Use OPENAI_CREATE_IMAGE to create images from text prompts using GPT Image or DALL-E models.
Tool: OPENAI_CREATE_IMAGE
Inputs:
- model: string (required) -- "gpt-image-1", "gpt-image-1.5", "dall-e-3", "dall-e-2"
- prompt: string (required) -- max 32000 chars (GPT Image), 4000 (DALL-E 3), 1000 (DALL-E 2)
- size: "1024x1024" | "1536x1024" | "1024x1536" | "auto" | "256x256" | "512x512" | "1792x1024" | "1024x1792"
- quality: "standard" | "hd" | "auto" | "high" | "medium" | "low"
- n: integer (1-10; DALL-E 3 supports n=1 only)
- background: "transparent" | "opaque" | "auto" (GPT Image models only)
- style: "vivid" | "natural" (DALL-E 3 only)
- user: string (optional)
4. List Available Models
Use OPENAI_LIST_MODELS to discover which models are accessible with your API key.
Tool: OPENAI_LIST_MODELS
Inputs: (none)
Known Pitfalls
| Pitfall | Detail |
|---|---|
| DALL-E deprecation | DALL-E 2 and DALL-E 3 are deprecated and will stop being supported on 05/12/2026. Prefer GPT Image models. |
| DALL-E 3 single image only | OPENAI_CREATE_IMAGE with DALL-E 3 only supports n=1. Use GPT Image models or DALL-E 2 for multiple images. |
| Token limits for embeddings | Input must not exceed 8192 tokens per item and 2048 items per batch for embedding models. |
| Reasoning model restrictions | temperature and top_p are not supported with reasoning models (o3-mini, etc.). Use reasoning.effort instead. |
| Structured output strict mode | When strict: true in json_schema format, ALL schema properties must be listed in the required array. |
| Prompt length varies by model | Image prompt max lengths differ: 32000 (GPT Image), 4000 (DALL-E 3), 1000 (DALL-E 2). |
Quick Reference
| Tool Slug | Description |
|---|---|
OPENAI_CREATE_RESPONSE | Generate text/multimodal responses with structured output support |
OPENAI_CREATE_EMBEDDINGS | Create text embeddings for search, clustering, and RAG |
OPENAI_CREATE_IMAGE | Generate images from text prompts |
OPENAI_LIST_MODELS | List all models available to your API key |
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