How to Use the Deep Talk MCP in LlamaIndex

Q: Can I filter Deep Talk data before indexing?

You can use the allowedtools filter when setting up the client. This ensures only the specific metadata you want gets added to your index.

Index your customer conversation data directly into LlamaIndex for RAG apps.

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Connect Deep Talk MCP to LlamaIndex

Create your Vinkius account to connect Deep Talk 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 Deep Talk with LlamaIndex

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Perplexity

Ground LlamaIndex RAG in Deep Talk data

Pipe `list_conversation_clusters` results straight into your vector store. Your index now contains actual semantic groupings from your customer base. This makes your RAG queries grounded in real API data. Your agent stops guessing and starts pulling context from the clusters it just retrieved.

Map sentiment for LlamaIndex queries

Run `get_sentiment_analytics` and store the scores in your index. You can then query your knowledge base for trends like negative sentiment spikes over time. This bridges the gap between raw conversation logs and queryable insights. Your index reflects the current state of your customer support interactions.

Index topic prevalence

Call `list_extracted_topics` to pull key themes into your LlamaIndex knowledge base. These topics become searchable nodes for your agent to reference. It keeps your knowledge current without manual updates. Your agent queries the index, finds the relevant themes, and generates answers based on the latest data.

Setup guide

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

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Common questions about Deep Talk MCP in LlamaIndex

You convert the tool output into a searchable index. Once indexed, your agent performs semantic searches across the conversation records.

It does. You use the McpToolSpec to generate the tool list and hand it to the FunctionAgent.

The data is persistent once indexed in your vector store. You control how long that information remains available for your agent to query.

All traffic uses encrypted tunnels. Your raw conversation logs never touch public storage and remain within the ephemeral sandbox.

You can use the allowed_tools filter when setting up the client. This ensures only the specific metadata you want gets added to your index.

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

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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