How to Use the BannerBite MCP in LlamaIndex

Q: How do I set up BannerBite with LlamaIndex?

Install llama-index-tools-mcp and set up a BasicMCPClient pointing to the Vinkius MCP endpoint. Wrap it with McpToolSpec and pass the async tool list to your FunctionAgent.

Q: Can LlamaIndex search my BannerBite projects?

Yes. You can configure a routine that runs listprojects and indexes the returned metadata. Your agent can then perform semantic searches across your available templates.

Q: How does LlamaIndex handle asynchronous video generation in BannerBite?

After triggering generatevideo, the agent receives a tracking ID. You can instruct the agent to pause and query getrender periodically until the video file is ready for download.

Q: What happens if the API connection drops?

Your agent should run getaccountcheck before starting a large batch. If the tool returns an error, LlamaIndex can halt the process and notify you before attempting to render anything.

Index your BannerBite templates and generate data-grounded marketing media through LlamaIndex.

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Connect BannerBite MCP to LlamaIndex

Create your Vinkius account to connect BannerBite to LlamaIndex 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 LlamaIndex

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RAG-Driven Media via MCP Server

Stop guessing which video template matches your documentation. When you connect this MCP Server to LlamaIndex, your agent indexes the output of `list_projects`. Now your application maps internal product specs directly to the right visual format using semantic search. You ask the agent for a social post about a new feature. It queries your vector store for the feature details, pulls the correct template ID, and fires off `generate_image`. The finished asset URL then becomes part of your queryable knowledge base.

Indexing Render Histories

Tracking hundreds of generated videos gets messy fast. LlamaIndex can ingest the results from `list_renders` and store them as searchable documents. If you need to find that specific holiday promo video from last month, you just ask. The agent retrieves the exact job details and uses `get_render` to pull the final download link. You bypass the BannerBite dashboard entirely. Your agent simply acts as a natural language search engine for your entire media library.

Context-Aware Scene Editing

Templates contain multiple layers and scenes that require precise data mapping. By calling `get_project` and `list_bites`, the agent pulls the exact structural requirements for a video. It cross-references these requirements against your indexed brand guidelines. Before calling `generate_media`, the agent knows exactly which text fields need filling and how many characters fit on screen. You get perfectly formatted visual assets that actually adhere to your internal design rules, generated entirely through MCP tool calls.

Setup guide

Set up BannerBite MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all BannerBite MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to BannerBite tools.",
)
response = await agent.run("List recent BannerBite data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by BannerBite. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about BannerBite MCP in LlamaIndex

Install `llama-index-tools-mcp` and set up a `BasicMCPClient` pointing to the Vinkius MCP endpoint. Wrap it with `McpToolSpec` and pass the async tool list to your `FunctionAgent`.

Yes. You can configure a routine that runs `list_projects` and indexes the returned metadata. Your agent can then perform semantic searches across your available templates.

After triggering `generate_video`, the agent receives a tracking ID. You can instruct the agent to pause and query `get_render` periodically until the video file is ready for download.

Your agent should run `get_account_check` before starting a large batch. If the tool returns an error, LlamaIndex can halt the process and notify you before attempting to render anything.

Your layer names, scene structures, and generated image URLs are processed strictly through a zero-trust V8 sandbox. Vinkius routes the payloads between the API and your local vector store without logging the actual creative assets during the MCP connection.

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