How to Use the Uniphore Conversation AI MCP in Google ADK

Q: How do I search for past conversations using Uniphore Conversation AI and Google ADK?

You use searchmeetings. You can pass in a keyword or topic, and the server finds all matching meeting IDs. This is crucial when you're running large-scale agent processes across your BigQuery data.

Q: What is the best way to get meeting context using Uniphore Conversation AI and Google ADK?

First, you should run listmeetings to discover all IDs. Then, use getmeeting with a specific ID to pull basic details. This ensures your agent knows exactly which conversation it's about before running heavier analytics.

Run massive-scale analysis of calls using the Google ADK for enterprise agents.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Uniphore Conversation AI MCP to Google ADK

Create your Vinkius account to connect Uniphore Conversation AI to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Uniphore Conversation AI with Google ADK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Getting Full Call Transcripts via MCP Server

The `get_transcript` tool provides the complete, raw text from any recorded call. Since you're working in an enterprise environment with BigQuery and Vertex AI, this full data stream allows your agent to perform deep, contextual analysis. It’s better than a summary because it gives you every single word spoken, letting Gemini models run their massive-context reasoning over the entire dataset.

Analyzing Meeting Insights with Google ADK

Use `get_meeting_analytics` to pull key insights and metrics for any call. These analytics are perfect for feeding into BigQuery tables, allowing your agent to compare performance across hundreds of meetings simultaneously. This mechanism helps you track trends over time—things like peak conversation times or common topics that emerge repeatedly.

Structured Data Retrieval with Google ADK

You can ask the server for specific deliverables, like using `get_action_items`. The result is structured so your agent doesn't just get a paragraph; it gets distinct records of tasks. This makes the data immediately usable in downstream workflows. This reliable structure means you spend less time cleaning up data and more time acting on it.

Setup guide

Set up Uniphore Conversation AI MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Uniphore Conversation AI tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Uniphore Conversation AI_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Uniphore Conversation AI tools via MCP.",
    tools=mcp_tools,
)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Uniphore. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Uniphore Conversation AI now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Uniphore Conversation AI MCP in Google ADK

You use `search_meetings`. You can pass in a keyword or topic, and the server finds all matching meeting IDs. This is crucial when you're running large-scale agent processes across your BigQuery data.

Absolutely. Because it uses a standard MCP interface, the Google ADK treats it as just another toolset. Your agents can easily call these functions alongside other Google Cloud services.

First, you should run `list_meetings` to discover all IDs. Then, use `get_meeting` with a specific ID to pull basic details. This ensures your agent knows exactly which conversation it's about before running heavier analytics.

The toolset itself provides clean, structured outputs for action items and summaries. When integrated into your agent pipeline, you can validate that output against expected schemas before passing it to Gemini models.

The server primarily touches conversation analytics and insights. This mechanism allows your agents to process aggregated metrics rather than raw, PII-laden transcripts, providing a good balance between depth and compliance.

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