OpenAI platform and APIs
View All TagsOpenAI's Sora can generate videos from text prompts. Seriously impressive stuff. But the API's a bit clunky to work with directly.
That's why we built the Sora MCP bundle. 6 tools that let you create, remix, and manage AI-generated videos without touching code. Just tell the AI what you want and it handles the rest.
This video was generated using the Sora MCP bundle—we used our own tools to create it.
Here's a problem I kept running into: when you're building an MCP server, you face this weird tension between giving AI agents enough control and not drowning them in options. Build 20 different tools and you're burning context window on redundant functionality. Build 3 tools with no parameters and the AI can't do anything useful.
After shipping dozens of MCP integrations, I found something that actually works: six core tools that balance OpenAI's single-string requirements with rich, parameter-driven operations. It's not arbitrary—there's a reason this number keeps working.
When OpenAI integrated support for Anthropic's Model Context Protocol (MCP) into ChatGPT's deep research feature, they documented something elegant: a two-tool pattern that gives AI agents a consistent way to engage with any data source. If your MCP server implements search and fetch with their specific signatures, ChatGPT knows exactly how to explore your data without custom integration code.
Both tools accept only a single string parameter. That constraint isn't a limitation—it's what makes the pattern universal.
OpenAI just made it clear: if you want your MCP server to work with ChatGPT's deep research capabilities, it needs to implement two specific tools: search and fetch.
This isn't optional. It's the protocol standard that enables ChatGPT to discover and retrieve information from external data sources. And if you're using MCPBundles, you already have it.
