How to Use the Cliengo MCP in CrewAI

Q: How do I add the Cliengo MCP Server to my CrewAI agents?

Pass the Vinkius URL into the mcps array when defining your Agent. For granular control, import MCPServerHTTP from crewai.mcp and apply a toolfilter. This restricts which agent can access specific endpoints.

Q: How do I restrict an agent to only read chat history?

Use the tool filter configuration to expose only getchathistory. The agent will be physically unable to list users or webhooks, keeping its scope tightly bound to the assigned task.

Deploy autonomous CrewAI agents to monitor, analyze, and escalate your Cliengo chat queues.

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Connect Cliengo MCP to CrewAI

Create your Vinkius account to connect Cliengo 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 Cliengo with CrewAI

Ask AI about this MCP

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Claude

Perplexity

Multi-agent lead qualification

The `list_cliengo_leads` tool on this MCP Server allows your researcher agent to pull the daily intake of new contacts. It hands that raw list over to an analyst agent to score the prospects based on your custom criteria. Deep diving into a specific profile uses `get_lead_details`. The analyst extracts the exact contact history and delegates the follow-up task to an outbound communication agent.

CrewAI conversational auditing

Fetching full session logs is handled by `get_chat_history`. A dedicated quality assurance agent reads the transcript to ensure your human operators followed standard operating procedures. Spot-checking specific users requires `get_contact_messages`. The QA agent compiles a report of negative interactions and passes it up the hierarchy to a manager agent for review.

Map internal agent capacity

Querying `list_cliengo_users` via MCP gives your CrewAI system visibility into your human workforce. A dispatcher agent checks who is currently active before assigning a complex support ticket. Auditing your active deployments happens via `list_cliengo_websites`. A technical agent verifies that the chat widgets are running on the correct domains and reports any discrepancies.

Setup guide

Set up Cliengo 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 Cliengo tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Cliengo 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="Cliengo Analyst",
        goal="Access and analyze Cliengo data via MCP.",
        backstory="Expert analyst with direct Cliengo access.",
        tools=mcp_tools,
    )

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

Pass the Vinkius URL into the `mcps` array when defining your Agent. For granular control, import `MCPServerHTTP` from `crewai.mcp` and apply a `tool_filter`. This restricts which agent can access specific endpoints.

The framework relies on shared memory to pass context between roles. The researcher agent pulls the contacts, and the writer agent instantly has access to those details for drafting emails.

The system supports stdio, SSE, and Streamable HTTP transports. Vinkius endpoints default to HTTP, making the connection process a single line of Python code.

Use the tool filter configuration to expose only `get_chat_history`. The agent will be physically unable to list users or webhooks, keeping its scope tightly bound to the assigned task.

The server transmits raw customer dialogue and lead contact details. Vinkius secures this pipeline using a V8 Isolate Sandbox that destroys the environment immediately after the task finishes. Your API keys never leave the secure vault.

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