How to Use the Uniphore Conversation AI MCP in CrewAI

Q: How do I use Uniphore Conversation AI to track topics with CrewAI?

The 'Researcher' agent calls listtopics first. This gives a master list of tracked keywords, which can then be used by other agents in the crew for targeted searching.

Q: Can Uniphore Conversation AI summarize meetings with CrewAI?

Yes. The 'Summary Agent' uses getmeetingsummary on a given meeting ID, and this result is immediately available to other agents in the crew for further analysis.

Q: What happens if I need raw transcripts with Uniphore Conversation AI and CrewAI?

The 'Archivist' agent calls gettranscript. This gives the full, granular text, which can then be passed to a different agent for sentiment analysis.

Q: How does CrewAI find meeting details using Uniphore Conversation AI?

The crew uses listmeetings first. Then, they use the resulting IDs in calls to getmeeting to build a comprehensive context for all agents.

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Setup Uniphore Conversation AI with CrewAI

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Coordinating Meeting Analysis with the MCP Server

A specialized 'Analyst' agent uses `get_meeting_analytics` to pull conversation insights. A separate 'Reporter' agent then takes those metrics and summarizes them using `get_meeting_summary`. CrewAI handles the shared memory, making sure the Reporter knows exactly what the Analyst found.

Autonomous Meeting Discovery with CrewAI

A 'Researcher' agent runs a sequence: first calling `list_meetings`, then passing potential IDs to `search_meetings`. This allows the crew to autonomously narrow down relevant conversations. It’s not just querying; it’s building an investigative pipeline.

Extracting Action Items for Follow-Up

You can assign a dedicated 'Action Agent' whose sole job is to call `get_action_items` on a specified meeting ID. This agent ensures that every conversation yields clear, follow-up tasks. The crew works together: one gets the summary, another pulls the actions.

Setup guide

Set up Uniphore Conversation AI MCP in CrewAI

Prerequisites

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

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Uniphore Conversation AI tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Uniphore Conversation AI Analyst",
    goal="Access and analyze Uniphore Conversation AI data via MCP.",
    backstory="Expert analyst with direct Uniphore Conversation AI access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Uniphore Conversation AI transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Uniphore Conversation AI Analyst",
        goal="Access and analyze Uniphore Conversation AI data via MCP.",
        backstory="Expert analyst with direct Uniphore Conversation AI access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Uniphore Conversation AI transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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.

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Common questions about Uniphore Conversation AI MCP in CrewAI

The 'Researcher' agent calls `list_topics` first. This gives a master list of tracked keywords, which can then be used by other agents in the crew for targeted searching.

Yes. The 'Summary Agent' uses `get_meeting_summary` on a given meeting ID, and this result is immediately available to other agents in the crew for further analysis.

The 'Archivist' agent calls `get_transcript`. This gives the full, granular text, which can then be passed to a different agent for sentiment analysis.

The crew uses `list_meetings` first. Then, they use the resulting IDs in calls to `get_meeting` to build a comprehensive context for all agents.

The server touches meeting transcripts and conversation analytics data. CrewAI's shared memory ensures that agents handle these records responsibly throughout the autonomous operation.

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