How to Use the Uniphore Conversation AI MCP in LlamaIndex

Q: How do I search topics using Uniphore Conversation AI and LlamaIndex?

First, use listtopics to get the list. Then, index that list into your vector store. You can then query the knowledge base for specific topic discussions.

Q: Can I get meeting summaries using Uniphore Conversation AI and LlamaIndex?

Yes. Use getmeetingsummary to fetch a summary, and immediately index that text. This makes the summary content searchable, allowing you to retrieve it months later with a simple query.

Q: What if I need action items from Uniphore Conversation AI and LlamaIndex?

Execute getactionitems for a meeting ID. The resulting structured data is then indexed, making it easily retrievable and queryable in your knowledge base.

Q: Does Uniphore Conversation AI support getting transcripts with LlamaIndex?

The gettranscript tool fetches the full transcript. Indexing this raw text means you can ask complex questions about the conversation's content, and the system will pull direct quotes from the stored data.

Index Uniphore Conversation AI data into a knowledge base using LlamaIndex.

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Connect Uniphore Conversation AI MCP to LlamaIndex

Create your Vinkius account to connect Uniphore Conversation AI 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 Uniphore Conversation AI with LlamaIndex

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The `get_transcript` tool provides raw meeting text. When you index this output, the full conversation becomes part of your vector store. This lets you query historical data semantically—you ask a question, and it finds the relevant snippet from past calls. It’s not just retrieval; the indexed transcript allows LlamaIndex to ground answers in actual API data, preventing hallucinations when answering complex questions.

Query meeting insights with LlamaIndex

You can index structured outputs like those from `get_action_items`. Instead of just reading a list, you query the knowledge base and ask: 'What action items were assigned to Marketing last quarter?' The RAG application combines API data with your question. The system indexes these records alongside documents, giving you one unified source for all organizational knowledge.

Discover topics using MCP Server

Use `list_topics` to fetch keywords and categories. Indexing this list creates a searchable catalog of organizational focus areas. This makes it easy to query, 'What meetings discussed Topic X last month?' without knowing the specific meeting ID. The resulting index allows users to quickly pinpoint relevant discussions across massive amounts of call data.

Setup guide

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

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Common questions about Uniphore Conversation AI MCP in LlamaIndex

First, use `list_topics` to get the list. Then, index that list into your vector store. You can then query the knowledge base for specific topic discussions.

Yes. Use `get_meeting_summary` to fetch a summary, and immediately index that text. This makes the summary content searchable, allowing you to retrieve it months later with a simple query.

Execute `get_action_items` for a meeting ID. The resulting structured data is then indexed, making it easily retrievable and queryable in your knowledge base.

The `get_transcript` tool fetches the full transcript. Indexing this raw text means you can ask complex questions about the conversation's content, and the system will pull direct quotes from the stored data.

This server touches meeting transcripts, summaries, and analytics. When you index this data using LlamaIndex, all inputs are contained within your private, queryable knowledge base.

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