Uniphore Conversation AI MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Uniphore Conversation AI as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Uniphore Conversation AI. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Uniphore Conversation AI?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Uniphore Conversation AI MCP Server
Connect Uniphore to any AI agent and unlock powerful conversation intelligence -- retrieve meeting transcripts, AI-generated summaries, action items, and analytics through natural conversation.
LlamaIndex agents combine Uniphore Conversation AI tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Meeting Transcripts -- Get speaker-tagged transcripts of any recorded call or meeting
- AI Summaries -- Retrieve concise summaries of key discussion points
- Action Items -- Extract next steps and tasks identified during meetings
- Conversation Analytics -- View talk ratios, sentiment, topics, and engagement metrics
- Search Meetings -- Find past meetings by keyword or topic discussed
The Uniphore Conversation AI MCP Server exposes 8 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Uniphore Conversation AI to LlamaIndex via MCP
Follow these steps to integrate the Uniphore Conversation AI MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from Uniphore Conversation AI
Why Use LlamaIndex with the Uniphore Conversation AI MCP Server
LlamaIndex provides unique advantages when paired with Uniphore Conversation AI through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Uniphore Conversation AI tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Uniphore Conversation AI tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Uniphore Conversation AI, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Uniphore Conversation AI tools were called, what data was returned, and how it influenced the final answer
Uniphore Conversation AI + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Uniphore Conversation AI MCP Server delivers measurable value.
Hybrid search: combine Uniphore Conversation AI real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Uniphore Conversation AI to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Uniphore Conversation AI for fresh data
Analytical workflows: chain Uniphore Conversation AI queries with LlamaIndex's data connectors to build multi-source analytical reports
Uniphore Conversation AI MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Uniphore Conversation AI to LlamaIndex via MCP:
get_action_items
Get action items extracted from a meeting
get_meeting
Get details of a specific meeting
get_meeting_analytics
Get conversation analytics and insights for a meeting
get_meeting_summary
Get the AI-generated summary of a meeting
get_transcript
Get the full transcript of a meeting
list_meetings
Use this to discover meeting IDs before querying specific details. List all recorded meetings and calls
list_topics
List all tracked topics and keywords in the organization
search_meetings
Search meetings by keyword or topic
Example Prompts for Uniphore Conversation AI in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Uniphore Conversation AI immediately.
"Show me the summary for meeting MTG-123."
"Get the transcript for meeting MTG-456."
"What are the action items from the last sales call?"
Troubleshooting Uniphore Conversation AI MCP Server with LlamaIndex
Common issues when connecting Uniphore Conversation AI to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpUniphore Conversation AI + LlamaIndex FAQ
Common questions about integrating Uniphore Conversation AI MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Uniphore Conversation AI with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Uniphore Conversation AI to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.