How to Use the Transloadit MCP in LlamaIndex

Q: How do I query cost usage with Transloadit and LlamaIndex?

You let your agent retrieve billing records using getbillingusage. The output data is then indexed, meaning you can ask natural language questions about the costs rather than just seeing a raw table.

Q: What is the best way to manage templates with Transloadit and LlamaIndex?

You use listtemplates or gettemplatedetails, which feeds the configuration into your knowledge base. This lets you build RAG applications that answer questions about template logic.

Query past runs and configurations with your AI client using LlamaIndex.

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Connect Transloadit MCP to LlamaIndex

Create your Vinkius account to connect Transloadit 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 Transloadit with LlamaIndex

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Indexing Assembly Results for Search

LlamaIndex makes Transloadit's operational history searchable. When you call `get_assembly_details`, the resulting status and outputs aren't just read—they become part of your knowledge index. You can query past runs to ground answers in actual API data. This is key for troubleshooting. Instead of guessing why a job failed, you ask the agent about a specific assembly ID, and it retrieves context from the indexed results.

Retrieving Configuration Details

The tool allows your AI client to index template definitions. Calling `get_template_details` or listing templates with `list_templates` means that configuration JSON is stored in a vector store. You can then ask natural language questions like, 'What was the resize rule for Template X?' The agent uses this indexed knowledge base to provide answers based on actual data, not just general documentation.

Tracking Billing and Usage History

Financial records become searchable. Passing a month via `get_billing_usage` generates usage and cost reports that LlamaIndex can index. You can then ask complex questions like, 'How much did we process images in March?' The system grounds your answers in the specific data returned by Transloadit's billing API.

Setup guide

Set up Transloadit 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 Transloadit 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 Transloadit tools.",
)
response = await agent.run("List recent Transloadit data")

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Common questions about Transloadit MCP in LlamaIndex

You let your agent retrieve billing records using `get_billing_usage`. The output data is then indexed, meaning you can ask natural language questions about the costs rather than just seeing a raw table.

Absolutely. After running an assembly, its status and results are indexed. You can query the system to retrieve specific operational details from past assemblies without needing the exact ID.

You use `list_templates` or `get_template_details`, which feeds the configuration into your knowledge base. This lets you build RAG applications that answer questions about template logic.

Yes, it handles encoding videos and resizing images as part of its core pipeline functionality. The results and configurations for these varied tasks are all captured and indexed by the MCP Server.

The server touches file processing usage/costs, assembly status/results, and media processing templates. All of these structured metadata types are optimized for indexing into a unified knowledge base.

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