OpenAI Automation - Automating OpenAI API Workflows

Skills Overview

OpenAI Automation is an automation tool that integrates with the Composio MCP to help developers simplify OpenAI API calling workflows. It supports multimodal response generation (text + image input), structured JSON output, creation of text embedding vectors, image generation with DALL-E and GPT Image, and querying of available model lists. It is suitable for scenarios such as RAG pipelines, vector search, and bulk content generation.

Use Cases

1. RAG Pipelines and Vector Search: Use OPENAI_CREATE_EMBEDDINGS to generate embedding vectors for documents, build semantic search and retrieval-augmented generation systems, and support batch processing of up to 2048 text items.

2. Multimodal Content Analysis: Use OPENAI_CREATE_RESPONSE to handle mixed image and text inputs for OCR recognition, image understanding, and complex reasoning tasks. It supports the latest models such as GPT-4o and o3-mini.

3. Batch Image Generation: Use OPENAI_CREATE_IMAGE to generate images automatically from text prompts. It supports GPT Image and DALL-E models and is suitable for bulk content creation, design prototypes, and marketing asset production.

Core Features

1. Generate Responses (Multimodal + Structured Output) Use the OPENAI_CREATE_RESPONSE tool to call models such as GPT-5, GPT-4o, or o3-mini. It supports pure text input or multimodal input with images + text. With text.format, you can generate strictly structured output that conforms to a JSON Schema, which is ideal for automation scenarios requiring exact data formats. Reasoning models support the reasoning.effort parameter to control the depth of thinking.

2. Create Embedding Vectors Use OPENAI_CREATE_EMBEDDINGS to convert text into high-dimensional vectors. It supports models such as text-embedding-3-small, text-embedding-3-large, and text-embedding-ada-002. Embedding vectors can be used for semantic search, document clustering, recommendation systems, and RAG applications. Each input supports up to 8192 tokens, and up to 2048 items can be processed in a single batch.

3. Generate Images Use OPENAI_CREATE_IMAGE to generate images from text descriptions. It supports GPT Image (gpt-image-1, gpt-image-1.5) and DALL-E (dall-e-3, dall-e-2) models. Image size, quality, and style (vivid/natural) can be customized. GPT Image models also support transparent backgrounds and longer prompt text (up to 32,000 characters).

Frequently Asked Questions

What is OpenAI Automation?

OpenAI Automation is a suite of tools provided via the Composio MCP (Model Context Protocol) server. It encapsulates common functionality of the OpenAI API, allowing you to directly call OpenAI response generation, embedding creation, image generation, and model management capabilities from an MCP-compatible client—without writing API-calling code.

How do I generate structured JSON output?

When using the OPENAI_CREATE_RESPONSE tool, specify a JSON Schema configuration in the text.format parameter. For example, set type: "json_schema", provide the schema definition (including type, properties, and required fields), and set strict to true. Note: When strict mode is enabled, all properties in the schema must be listed in the required array.

What’s the difference between DALL-E 3 and GPT Image?

DALL-E 3 has been marked as deprecated and will stop support on May 12, 2026, and it can generate only one image per request (n=1). GPT Image models (gpt-image-1, gpt-image-1.5) are OpenAI’s recommended next-generation image generation models. They support generating multiple images in a single request (up to 10), offer transparent background options, and allow longer prompt text (up to 32,000 characters).