Transloadit MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Transloadit as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Transloadit. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Transloadit?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Transloadit MCP Server
Transform your AI agent into a full-scale media orchestration engine by connecting it to your Transloadit application. Eliminate complicated dashboards and rely entirely upon conversational instructions to launch, monitor, or abort heavy "Assemblies" (media jobs), track operational bills, and register scalable JSON templates without manual interactions.
LlamaIndex agents combine Transloadit tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Assembly Execution — Feed a JSON steps configuration directly into your agent to prompt complex file encodings, resizing jobs, or transmutations natively
- Live Monitoring — Interrogate the agent to retrieve exact results, completion statuses, or outputs parameters for any active or past processing task (Assembly)
- Template Management — Build reusable encoding architectures (Templates) through chat instructions, list available pipelines, or delete deprecated presets
- Error Recovery — Immediately command the AI to cancel heavily hung/unnecessary processing batches or instruct a strict "job replay" to re-attempt a failed cycle
- Resource Tracking — Access your precise billing usage and operational costs per specific month natively without hunting reports on the dashboard
The Transloadit MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Transloadit to LlamaIndex via MCP
Follow these steps to integrate the Transloadit MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Transloadit
Why Use LlamaIndex with the Transloadit MCP Server
LlamaIndex provides unique advantages when paired with Transloadit through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Transloadit tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Transloadit tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Transloadit, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Transloadit tools were called, what data was returned, and how it influenced the final answer
Transloadit + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Transloadit MCP Server delivers measurable value.
Hybrid search: combine Transloadit real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Transloadit to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Transloadit for fresh data
Analytical workflows: chain Transloadit queries with LlamaIndex's data connectors to build multi-source analytical reports
Transloadit MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Transloadit to LlamaIndex via MCP:
cancel_assembly
This action is final. Aborts a running Transloadit assembly
create_assembly
Provide a steps JSON defining the pipeline. Creates a Transloadit assembly for automated file processing
create_processing_template
Provide a name and the steps JSON. Creates a reusable JSON template for file processing
delete_template
This action is irreversible. Permanently deletes a processing template
get_assembly_details
Retrieves the status and results of a specific Transloadit assembly
get_billing_usage
Pass the month in YYYY-MM format. Retrieves file processing usage and costs for a specific month
get_template_details
Retrieves the configuration of a specific Transloadit template
list_assemblies
Lists recent Transloadit assemblies in the account
list_templates
Lists all saved processing templates
replay_assembly
Re-runs a completed Transloadit assembly
Example Prompts for Transloadit in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Transloadit immediately.
"Fetch the billing usage details for January 2026."
"Cancel the running assembly calculation currently at ID b13a4x2."
Troubleshooting Transloadit MCP Server with LlamaIndex
Common issues when connecting Transloadit to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTransloadit + LlamaIndex FAQ
Common questions about integrating Transloadit MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Transloadit with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Transloadit to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.