How do I connect AI image and video generation to an AI agent?
Use MCP, the Model Context Protocol, which is how agents such as Claude and Cursor discover and call external tools. Connecting an MCP server that exposes generation workflows lets an agent run them directly, without you writing a bespoke integration per agent. The alternative — wrapping a REST API in custom tool definitions — works but has to be redone for each agent framework.
Why MCP rather than a custom wrapper
An MCP server describes its own tools, so any MCP-capable agent can discover what is available and how to call it without prior knowledge. That is the difference from a REST API, which needs a hand-written tool definition per agent framework and a rewrite whenever the surface changes. For generation specifically, this means an agent can list available models, submit a job and read back the result as ordinary tool calls.
What to expose to an agent
Exposing raw model calls gives an agent maximum flexibility and maximum opportunity to spend credits badly. Exposing saved workflows is usually better: the agent picks a pipeline and supplies inputs, while the sequencing, model choice and cost profile stay under your control. This is the same reasoning behind giving a colleague a template rather than a blank canvas and a budget.
Cost and safety boundaries
Generation costs real money per call, so an agent loop that retries aggressively can burn a balance quickly. Set the boundaries at the platform rather than trusting the prompt: constrain which workflows are callable, and prefer pipelines whose per-run cost you already know. Treat generation as a side-effecting tool in the same category as sending an email, not as a free read.
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