How to Use the Dyte MCP in LlamaIndex

Q: Can LlamaIndex agents create Dyte meetings on the fly?

Yes, the agent executes createmeeting when a user query requires a new video room. The resulting meeting ID is immediately cached and indexed in your local vector store.

Q: How do I search through past Dyte sessions using LlamaIndex?

The agent calls listmeetings to grab session histories, indexes the metadata, and performs semantic search. This lets you ask natural language questions about past video events.

Q: Does this MCP Server support resource loading in LlamaIndex?

Yes, you can set includeresources=True to allow direct data access. This gives your agent the ability to read meeting configurations directly as structured documents.

Index your live Dyte meeting metadata directly into LlamaIndex vector stores for semantic search.

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Connect Dyte MCP to LlamaIndex

Create your Vinkius account to connect Dyte 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 Dyte with LlamaIndex

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Index active meeting metadata into your vector store

`list_meetings` retrieves the history of all video sessions so your LlamaIndex agent can index them for future queries. This turns raw session lists into searchable knowledge bases that prevent information loss. Your agent queries this index to find specific session contexts instead of guessing. You get grounded answers based on actual meeting logs rather than LLM hallucinations.

Track participant history using LlamaIndex MCP tools

`list_participants` extracts the roster of users who attended specific video calls to build an attendee database. Your agent runs semantic searches over this data to map user engagement across different dates. By calling `get_meeting` alongside participant lists, the agent creates a unified index of who met when. This structure allows you to query meeting structures directly from your RAG pipelines.

Archive recording logs for semantic search

`get_recording` fetches the files and metadata of completed sessions to feed your knowledge index. The agent stores these references in your vector database for easy retrieval during user queries. When users ask about past discussions, the agent searches the index and provides the exact recording URL. This integrates live video assets directly with your document retrieval systems.

Setup guide

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

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Common questions about Dyte MCP in LlamaIndex

Install llama-index-tools-mcp and initialize the BasicMCPClient with your Vinkius URL. Wrap it in McpToolSpec and convert it to a tool list for your FunctionAgent.

Yes, the agent executes create_meeting when a user query requires a new video room. The resulting meeting ID is immediately cached and indexed in your local vector store.

The agent calls list_meetings to grab session histories, indexes the metadata, and performs semantic search. This lets you ask natural language questions about past video events.

Yes, you can set include_resources=True to allow direct data access. This gives your agent the ability to read meeting configurations directly as structured documents.

Vinkius processes meeting IDs and participant tokens within a secure, zero-trust MCP sandbox. The actual video data stays on Dyte's servers, while only the metadata you choose to index is sent to your vector store.

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