How to Use the Verbit MCP in LlamaIndex

Q: How can LlamaIndex use Verbit for historical transcriptions?

You index the resulting transcripts downloaded via gettranscript. This turns past job results into searchable documents your RAG application can query.

Q: Does the MCP Server support filtering resources for LlamaIndex?

Yes, you can use allowedtools filters to restrict which Verbit capabilities your agent sees, keeping the scope of the index focused on specific tasks.

Build RAG applications on transcription data using LlamaIndex.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Verbit MCP to LlamaIndex

Create your Vinkius account to connect Verbit 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.

GDPR Free for Subscribers

Setup Verbit with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

LlamaIndex: Indexing Transcribed Media Data

The power here is turning transient job status into permanent knowledge. After a successful transcription job, you can feed the raw text output from `get_transcript` directly into your index. This means users can query past transcriptions or job metadata using natural language search against the indexed content.

MCP Server: Grounding Answers in Live API Data

When building RAG applications, you need data that isn't hallucinated. Verbit provides this by letting your agent use `get_job` status updates as sources of truth for your index. Instead of guessing if a job is done, the system queries the actual API state and indexes the resulting confirmation.

LlamaIndex: Querying Historical Transcription Jobs

The `get_job` tool's output can be indexed to create searchable records of past tasks. You can query, 'What was the status of media X last Tuesday?' and get an answer grounded in Verbit’s actual history. This allows you to combine document retrieval with live API data access seamlessly.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Verbit. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Verbit now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Verbit MCP in LlamaIndex

You index the resulting transcripts downloaded via `get_transcript`. This turns past job results into searchable documents your RAG application can query.

The system indexes the text content from completed transcriptions, as well as metadata like job IDs and completion timestamps, providing granular context for search.

Verbit deals with your media files and the resulting transcript text. The MCP Server ensures this sensitive content is indexed only after explicit user consent.

Yes, you can use `allowed_tools` filters to restrict which Verbit capabilities your agent sees, keeping the scope of the index focused on specific tasks.

Absolutely. You build a unified queryable index where external documents and live API data from Verbit are combined for answering questions.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python