How to Use the Deepgram MCP in LlamaIndex

Index your audio data into LlamaIndex for searchable knowledge bases driven by Deepgram.

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Connect Deepgram MCP to LlamaIndex

Create your Vinkius account to connect Deepgram 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 Deepgram with LlamaIndex

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Turn audio into searchable LlamaIndex nodes

Run `transcribe_audio_url` to convert media into text for your vector store. Your index now contains the actual spoken content from your files. This makes your knowledge base aware of audio data. You query past transcripts using standard semantic search patterns.

Manage Deepgram projects from LlamaIndex

Call `list_deepgram_projects` to pull your environment details into the agent context. You see all active projects without leaving your development environment. Use this to switch between different data buckets. It keeps your indexing logic organized and tied to the correct project.

Optimize models for LlamaIndex RAG

Check `list_available_models` to pick the best fit for your indexing strategy. Matching the model to your data type improves retrieval accuracy significantly. Configure your agents to prefer specific models. You ensure consistent results across your entire document repository.

Setup guide

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

You turn raw sound files into searchable text nodes. This connects your audio archives to the reasoning power of your index.

We provide a tool spec that maps our functions into your agent. You initialize the client, convert the tools to a list, and attach them to your FunctionAgent.

Absolutely. Once the transcription returns text, LlamaIndex treats it like any other document for embedding and retrieval.

Your audio and transcriptions are processed in an ephemeral sandbox. No data persists on our servers once the request finishes.

You can restrict which tools the agent sees. This keeps your index clean and prevents unauthorized modifications to your API keys.

Use it with your favorite AI tools

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