How to Use the RenderMe MCP in LlamaIndex

Q: How does LlamaIndex store metadata from RenderMe templates?

The framework runs listvideotemplates and converts the output into document nodes. These nodes are indexed into your vector store, allowing your agent to find templates using semantic search over the MCP data.

Q: Can I query my past RenderMe video jobs using LlamaIndex?

Yes, by indexing the output of listrecentrenderjobs. Your agent queries this index to check which videos succeeded, what templates were used, and when they were created.

Q: How do I verify my API connection is active in LlamaIndex?

The agent runs checkapihealth as a startup tool before executing index updates. This ensures your LlamaIndex pipeline doesn't fail mid-run due to credential or network issues.

Q: How do I organize my media assets before rendering?

Use listassetfolders to fetch your folder structure, then use those folder IDs with listuploadedassets to filter your media files. This keeps your asset selection clean and organized.

Index RenderMe video assets and templates into your LlamaIndex vector store for context-rich generation.

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Index your video assets for semantic search

The `list_uploaded_assets` tool pulls your entire media library directly into LlamaIndex to build a searchable knowledge index. Your agent queries this local index to find specific images or video clips instead of scanning raw directories every time. By combining folder metadata from `list_asset_folders` with your vector store, the agent locates assets using natural language. This turns a messy pile of media files into an organized, queryable asset library that feeds directly into your rendering steps.

Ground your video generation in actual MCP Server templates

The `list_video_templates` tool exposes your deployment configurations so LlamaIndex can index your available video designs. Your agent searches this MCP index to select the best layout based on the user's intent. Once selected, the agent calls `get_template_details` to retrieve the exact schema requirements. This grounding prevents the agent from hallucinating variable names when constructing the payload for `create_video_render_job`.

Track rendering history and account metrics

The `list_recent_render_jobs` tool retrieves your past video runs to feed historical render data into your LlamaIndex query engine. This lets you ask questions about past video runs and get answers grounded in real execution logs. You can also query `get_account_render_stats` to track your monthly usage patterns directly through your LLM. The agent parses these stats alongside your active projects from `list_video_projects` to optimize your rendering budget.

Setup guide

Set up RenderMe 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 RenderMe 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 RenderMe tools.",
)
response = await agent.run("List recent RenderMe 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 RenderMe. 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 RenderMe MCP in LlamaIndex

The framework runs `list_video_templates` and converts the output into document nodes. These nodes are indexed into your vector store, allowing your agent to find templates using semantic search over the MCP data.

Yes, by indexing the output of `list_recent_render_jobs`. Your agent queries this index to check which videos succeeded, what templates were used, and when they were created.

The agent runs `check_api_health` as a startup tool before executing index updates. This ensures your LlamaIndex pipeline doesn't fail mid-run due to credential or network issues.

Use `list_asset_folders` to fetch your folder structure, then use those folder IDs with `list_uploaded_assets` to filter your media files. This keeps your asset selection clean and organized.

All communication runs through a zero-trust MCP container on Vinkius that processes your data ephemerally. Your project names and rendering logs are never cached or stored on external servers outside of the secure runtime environment.

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