Integrating AI Image and Video Generation into Agents via MCP

Why Generative Content Capabilities Need to Be Integrated into Agents

Image and video generation models have advanced rapidly, from text-to-image and image-to-image, to reference-image-driven video synthesis and product-page-driven ad creative generation. The model-side capabilities are now mature. However, in real creative and marketing workflows, developers often encounter a common friction point: model capabilities are disconnected from the working environment.

Typical pain points include:

1. Context fragmentation: Developers discuss requirements and refine prompts inside an agent or code editor, but must switch to a separate web tool to manually submit generation tasks, then manually download and re-upload results. This is a long, error-prone chain.
2. Prompt transfer loss: Details accumulated in natural language conversations (brand tone, composition, style constraints) are easily lost when moving across tools.
3. Lack of automation: In marketing scenarios, requests like “give me a product introduction video” are essentially pipelines of “link → scrape → construct prompt → generate → return”. Manual operation cannot scale.

The MCP (Model Context Protocol) provides a standardized channel to solve these issues. It defines a common calling protocol between models/agents and external tools, allowing any generative capability to be encapsulated as a tool that agents can discover and invoke.

Positioning and Overall Architecture of the MCP Server

An MCP server for image and video generation is essentially a protocol-based encapsulation layer over generation model capabilities. Its architecture typically consists of four layers:

1. Protocol access layer: Implements the MCP protocol, exposes the tool list, input/output schemas, and invocation entry points to agents. It is compatible with common MCP hosts (agent clients, AI coding tools, command-line agents, etc.).
2. Orchestration and scheduling layer: Receives natural language instructions or structured parameters from the agent, performs intent parsing, prompt engineering, and task orchestration, deciding which model to call and whether to run tasks serially or in parallel.
3. Model adaptation layer: Connects to multiple generation models (text-to-image, image-to-image, text-to-video, image-to-video, etc.), abstracts away API differences, and unifies authentication, rate limiting, and retry policies.
4. Result return layer: Returns generated image/video resources directly into the current conversation context, avoiding the intermediate “download then upload” step.

Example: Tool Definition Structure

{
  "name": "generate_video",
  "description": "Generate video assets from a product link, reference image, or text prompt",
  "inputSchema": {
    "type": "object",
    "properties": {
      "prompt": { "type": "string", "description": "Text prompt" },
      "reference_image_url": { "type": "string" },
      "product_page_url": { "type": "string" },
      "aspect_ratio": { "type": "string", "enum": ["16:9", "9:16", "1:1"] }
    }
  }
}

Once the agent discovers this tool via MCP, it can trigger the call directly in conversation by passing parameters.

Core Capability Design

1. Conversational Generation

Users describe their needs in natural language inside the agent’s chat window, for example, “generate a vertical poster from this description.” The agent maps the natural language to structured tool-call parameters. The value of this design:

• Zero switching cost: Demand discussion and content production happen in the same context, reducing cognitive load.
• Iterative refinement: Users can directly say “make the image darker” in the same conversation, and the agent performs incremental modifications based on previous parameters, forming a natural creative iteration loop.

2. Multimodal Input Support

Mature generation workflows rarely rely on text only. An MCP server typically needs to support three categories of input:

• Product page URLs: The server scrapes page content, extracts product name, selling points, and visual assets, then automatically constructs prompts and storyboard scripts.
• Reference images: Support image-to-image or image-to-video generation while maintaining style and subject consistency.
• Plain text prompts: The classic text-to-content path.

From an engineering perspective, URL input requires page parsing and asset extraction (often combined with generic web scraping and HTML parsing capabilities), and the scraped results should be wrapped in prompt templates to ensure alignment between generated output and product information.

3. Unified Scheduling of Multiple Models

Different generation models have different strengths: some excel at high-quality static images, others at long-shot videos, and some perform better on text rendering or subject reference consistency. A key design goal of the MCP server is one-click invocation of multiple models with a unified abstraction:

• Expose a unified calling interface to the agent; the orchestration layer routes tasks to suitable models based on task type (image/video), resolution, and duration requirements.
• Provide users with simple model selection semantics, such as “use the model suitable for ads” or explicitly specify a model category.
• Handle authentication, quotas, and asynchronous task polling uniformly (video generation is typically a long-running asynchronous task requiring task ID polling and status callbacks).

4. In-Context Result Delivery

Generated results are returned directly to the current conversation context as resource references (file references or URLs). The agent can then perform secondary processing on the results (e.g., generate variants, concatenate storyboards). Users do not need to leave the chat window to complete the “generate — view — feedback — modify” loop. This is the core experience advantage of MCP over web-based tools.

Typical Use Case: Automated Marketing Video Production

Taking marketing asset production as an example, a complete automated pipeline looks like this:

1. The user provides a product landing page link.
2. The MCP server scrapes the page and extracts key product information and image assets.
3. The orchestration layer generates a storyboard script based on prompt templates (opening, selling points display, call to action).
4. The server invokes video generation models to produce each segment, optionally using an image model for cover frames.
5. The results are returned to the conversation window, where the user can iterate or directly deliver the assets.

Combined with general cloud capabilities (object storage for generated artifacts, message queues for decoupling asynchronous tasks, logging and monitoring services for tracing call chains), this flow can be productized into a sustainable asset production line.

Engineering Practice Recommendations

• Asynchronous task management: Video generation can take minutes. Implement task status polling or callback mechanisms, and define timeout semantics clearly in the MCP tool description.
• Cost and rate limiting control: Generative API calls are costly. Implement quota management and concurrency caps on the server side to prevent agent retry loops from causing uncontrolled expenses.
• Prompt governance: Consolidate common styles and brand constraints into reusable prompt templates to improve output consistency and reusability.
• Safety and compliance: Validate and review the domain of scraped URLs, and run compliance checks on generated results before delivery.

Conclusion

The MCP protocol is converging fragmented AIGC capabilities into unified agent workflows. Through a well-designed generation-oriented MCP server, developers can build a complete closed loop on top of the protocol — covering requirement understanding, multimodal input, multi-model generation, result delivery, and iterative refinement — making image and video generation a readily available foundational capability in any conversation.