How to Use the CData Connect Cloud MCP in CrewAI

Q: Can I restrict which CrewAI agents access CData Connect Cloud?

Yes. Use MCPServerHTTP from crewai.mcp and apply a toolfilter. You can give your research agent query access while restricting connection creation to your manager agent.

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Connect CData Connect Cloud MCP to CrewAI

Create your Vinkius account to connect CData Connect Cloud 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 CData Connect Cloud with CrewAI

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Coordinate database analysis with this MCP Server

Data retrieval for your research agents depends on `cdata_execute_query`. One agent pulls raw metrics from the backend proxy, while a separate analyst agent processes the results. This division of labor keeps context windows clean and prevents a single LLM from losing track of complex SQL logic. A mapping agent uses `cdata_get_schema_metadata` to build a complete graph of available interactions. It then passes this structural knowledge to the execution team, ensuring they write valid queries against the downstream database. Specialized tools make understanding the underlying architecture possible.

Audit proxy connections automatically

Dumping the entire array of external data sources is the job of `cdata_list_connections`. A monitoring agent pairs this with `cdata_test_connection` to ping every endpoint sequentially. If a link drops, the agent escalates the failure to a manager agent for immediate remediation. Hidden dependencies emerge when exploring internal link structures. An auditing agent calls `cdata_list_tables` and `cdata_get_table_columns` to map exactly what fields are exposed through the proxy. It logs any newly added tables that violate your internal security policies.

Provision workspaces hierarchically

Configuring new backend data sources happens via `cdata_create_connection` exposed by the MCP Server. A manager agent receives a natural language request, delegates the task to a provisioning agent, and verifies the setup. The entire configuration process happens autonomously based on your defined rules. The team uses `cdata_list_workspaces` to enumerate all logical virtual environments before assigning a new connection. This ensures tenant data remains strictly isolated across different departments. Proper segmentation of these new sources prevents organizational chaos.

Setup guide

Set up CData Connect Cloud 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 CData Connect Cloud tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent CData Connect Cloud 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 CData Connect. 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 CData Connect Cloud MCP in CrewAI

Pass your Vinkius endpoint directly into the mcps array when defining your agent. The framework automatically fetches the available database tools and attaches them to the agent capabilities.

Yes. Use MCPServerHTTP from crewai.mcp and apply a tool_filter. You can give your research agent query access while restricting connection creation to your manager agent.

The framework excels at delegation. A manager agent can plan a complex database migration, assigning schema mapping to one subordinate and data extraction to another.

The executing agent observes the error message and attempts to fix the SQL syntax automatically. It uses its internal reasoning loop to retry the operation before escalating the failure to the manager.

Authentication happens entirely outside the agent's view. Vinkius manages the proxy tokens within an isolated sandbox, meaning your Python scripts never touch the raw database passwords or API keys.

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