How to Use the BannerBite MCP in Pydantic AI

Q: How do I configure BannerBite for Pydantic AI?

Instantiate an MCPToolset with your Vinkius HTTP endpoint URL. Pass that toolset into your Agent constructor to expose the rendering tools to your model.

Q: Can my Pydantic AI agent test the BannerBite connection?

Hitting the getaccountcheck tool returns a basic status response. The agent uses this to confirm the Vinkius endpoint is active before starting a batch job.

Q: How does the agent find the right BannerBite template?

It runs listprojects to grab an array of available designs. You define a Pydantic model to parse that array and extract the correct project ID based on your criteria.

Type-safe image and video generation for Pydantic AI. Catch malformed BannerBite render requests before execution.

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Connect BannerBite MCP to Pydantic AI

Create your Vinkius account to connect BannerBite to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup BannerBite with Pydantic AI

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Perplexity

Type-safe media via Pydantic AI

Passing the wrong variable type to a video renderer usually results in a silent failure and a wasted API credit. Pydantic AI validates every argument your agent tries to send to `generate_media` against strict runtime models. If the agent attempts to pass an integer where a string is required for a text overlay, the framework fails loudly. You catch the validation error immediately instead of waiting for a broken video to finish rendering.

Inspect template schemas safely

Discovering the requirements for a specific video template means requesting the raw data first. The agent calls `get_project` to pull the structure, and Pydantic ensures the returned JSON matches your expected schema. Drilling down further requires calling `list_bites` to see individual scenes. Because the MCP Server handles the raw API communication, your agent only deals with clean, validated Python objects.

Poll render status with this MCP Server

You need a reliable way to know when an asynchronous image generation job completes. The agent hits `get_render` with the job ID, returning the exact state of the processing queue. Tracking multiple jobs at once happens through `list_renders`. The agent requests the recent history, and your Pydantic models guarantee the status fields always contain valid enum values like completed or failed.

Setup guide

Set up BannerBite MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "bannerbite-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to BannerBite tools.",
)

result = await agent.run("List recent BannerBite transactions")
print(result.output)

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Common questions about BannerBite MCP in Pydantic AI

Instantiate an `MCPToolset` with your Vinkius HTTP endpoint URL. Pass that toolset into your Agent constructor to expose the rendering tools to your model.

Video rendering parameters are complex and highly nested. Forcing the agent to validate its `generate_video` payloads against Pydantic models prevents expensive rendering errors.

Hitting the `get_account_check` tool returns a basic status response. The agent uses this to confirm the Vinkius endpoint is active before starting a batch job.

It runs `list_projects` to grab an array of available designs. You define a Pydantic model to parse that array and extract the correct project ID based on your criteria.

Scene definitions and render payloads exist only for the duration of the function call. The Vinkius infrastructure processes the HTTP request in an isolated sandbox and destroys all memory state immediately after routing.

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