How to Use the Dalil AI MCP in CrewAI

Deploy specialized autonomous agents to run Middle Eastern outreach campaigns using CrewAI.

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Connect Dalil AI MCP to CrewAI

Create your Vinkius account to connect Dalil 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 Dalil AI with CrewAI

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Delegate CRM research to specialized agents

Autonomous operations require dedicated roles. You can assign a researcher agent to constantly pull market data using the `list_companies` tool. This agent maps out target accounts across the region without requiring your input. The researcher hands its findings over to a data enrichment agent. That second agent runs `list_contacts` to find key decision-makers within those companies. They share memory automatically, building a complete picture of the target organization.

Analyze intent with CrewAI memory

Raw contact lists mean nothing without context. An analyst agent takes the enriched profiles and executes `get_contact_signals` to evaluate Arabic-native buying intent. It scores each prospect based on cultural cues and historical data. These insights get stored in the crew's shared context. The moderator agent reviews the scores to decide which leads are worth pursuing. High-priority targets get pushed to the execution queue immediately.

Run autonomous Dalil MCP Server outreach

Execution handles the final step of the pipeline. Your outreach agent drafts culturally appropriate messages and fires them off via `send_linkedin_message`. It knows exactly what to say because it read the analyst's notes from the shared memory. Monitoring happens concurrently. A separate supervisor agent periodically runs `list_campaigns` to track response rates. If a specific approach fails, the supervisor instructs the outreach agent to pivot its messaging strategy.

Setup guide

Set up Dalil 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 Dalil AI tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Add your Vinkius endpoint directly to the mcps array in your Agent definition. The framework automatically parses the available tools. Your agents will understand how to use them based on their assigned roles.

Role-based access keeps your crew organized. Use MCPServerHTTP with a tool_filter to expose specific operations. Give the researcher the listing tools, and only let the outreach agent send messages.

Shared memory handles the context transfer automatically. When one agent fetches buying signals via the MCP Server, the results enter the crew's internal state. The next agent in the sequence reads that data before taking action.

A manager agent can coordinate the entire outreach process. It reviews the company lists and delegates specific contact creation tasks to subordinates. You get a fully autonomous Middle Eastern sales team.

Everything routes through a secure, stateless environment. The platform provisions a temporary V8 Isolate to execute the messaging tool and destroys it instantly. Your proprietary outreach strategies remain completely isolated from other tenants.

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