How to Use the Uniphore Conversation AI MCP in Mastra AI

Q: How do I handle meeting data retrieval using Uniphore Conversation AI and Mastra AI?

You can build a reliable sequence: first, listmeetings to find the ID. Second, call getmeeting. Then, pass that ID to tools like gettranscript or getactionitems within your workflow.

Q: Can Uniphore Conversation AI manage topic tracking in Mastra AI workflows?

Absolutely. You can use the listtopics tool to discover tracked keywords first. This output then guides a subsequent search using searchmeetings.

Q: Can I use getmeeting details within Uniphore Conversation AI and Mastra AI?

Yes. You retrieve basic meeting context using getmeeting, which provides foundational data to start the next step in your complex workflow.

Build reliable, multi-step workflows: Uniphore Conversation AI for Mastra AI.

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Connect Uniphore Conversation AI MCP to Mastra AI

Create your Vinkius account to connect Uniphore Conversation AI to Mastra AI 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 Mastra AI

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Perplexity

Executing Complex Workflows with the MCP Server

Mastra AI's workflow engine lets you chain actions together. For example, if a meeting summary is vague, the agent can automatically call `get_transcript` to get raw text for better analysis. This multi-step process handles failure gracefully; if one tool call fails, your workflow retries with exponential backoff.

Getting Meeting Analytics and Summaries

Use `get_meeting_analytics` to pull conversation insights. You can then use the resulting data structure as input for a subsequent step that calls `get_meeting_summary`. Mastra AI manages this dependency chain automatically, ensuring you always have fresh context for each stage.

Searching and Listing Meetings via Mastra AI

Start by listing all available meetings using `list_meetings` to get a pool of IDs. Then, run the search step with `search_meetings`, passing keywords or topics into the tool call. Mastra AI handles the initial ID discovery and subsequent filtering in one robust workflow.

Setup guide

Set up Uniphore Conversation AI MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Uniphore Conversation AI tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "uniphore-conversation-ai-mcp-client",
  servers: {
    "uniphore-conversation-ai-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Uniphore Conversation AI Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Uniphore Conversation AI tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Uniphore Conversation AI transactions"
);
console.log(result.text);

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Common questions about Uniphore Conversation AI MCP in Mastra AI

You can build a reliable sequence: first, `list_meetings` to find the ID. Second, call `get_meeting`. Then, pass that ID to tools like `get_transcript` or `get_action_items` within your workflow.

Absolutely. You can use the `list_topics` tool to discover tracked keywords first. This output then guides a subsequent search using `search_meetings`.

Mastra AI automatically retries the call. If the failure persists, you can trigger a notification step to an admin, ensuring no data point is missed.

Yes. You retrieve basic meeting context using `get_meeting`, which provides foundational data to start the next step in your complex workflow.

The server accesses meeting transcripts and associated conversation analytics data. Mastra AI ensures these sensitive records are processed through controlled, reliable steps.

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