How to Use the Observe.AI MCP in CrewAI

Q: Can my CrewAI agents share Observe.AI data?

Yes. CrewAI uses shared memory to pass results from listinteractionsummaries between agents, letting them build on each other's work.

Deploy autonomous agent crews with Observe.AI to scale your contact center monitoring.

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Connect Observe.AI MCP to CrewAI

Create your Vinkius account to connect Observe.AI to CrewAI 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 Observe.AI with CrewAI

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Multi-agent QA crews with Observe.AI

Assign specialized agents to different parts of your QA process. One agent monitors `list_interactions` to flag anomalies, while another uses `get_interaction_transcript` to perform deep analysis. This delegation speeds up your audit cycles. Your crew works in parallel to cover more ground than a single human or agent ever could.

Role-based interaction analysis

Define agents with specific goals like 'Compliance Officer' or 'Customer Success Lead'. Each agent uses `list_qa_evaluations` to fulfill its unique mandate within your crew. This allows for granular control over how your team evaluates performance. You get different perspectives on the same interaction data without manual intervention.

Autonomous coaching and reporting

Let your crew manage the entire coaching lifecycle. Agents can identify gaps via `list_interaction_moments` and automatically create entries in `list_coaching_sessions` for review. This maintains a consistent feedback loop for your staff. Your agents handle the routine data entry while you focus on the high-level performance trends.

Setup guide

Set up Observe.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 Observe.AI tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Observe.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 →

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="Observe.AI Analyst",
        goal="Access and analyze Observe.AI data via MCP.",
        backstory="Expert analyst with direct Observe.AI access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Observe.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 Observe.AI. 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 Observe.AI MCP in CrewAI

You pass the server URL to the agent's MCP configuration. You can use a tool filter to ensure only specific agents access the QA tools.

Yes. CrewAI uses shared memory to pass results from `list_interaction_summaries` between agents, letting them build on each other's work.

The server treats each tool call as an independent request. Your crew can make multiple concurrent calls to the API.

Your API token is managed by the Vinkius MCP server. Your agents never see your raw credentials.

Only the agents in the active crew have access. Once the crew finishes the task, the transcripts are purged from the agent's context.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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