How to Use the Materialize (Streaming SQL DB) MCP in CrewAI

Q: How do I restrict a CrewAI agent from creating Materialize (Streaming SQL DB) clusters?

Apply a toolfilter in your configuration. Give your analyst agent access to querying tools while blocking it from calling createcluster.

Deploy autonomous CrewAI teams to monitor and manage your streaming SQL pipelines.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Materialize (Streaming SQL DB) MCP to CrewAI

Create your Vinkius account to connect Materialize (Streaming SQL DB) 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.

GDPR Free for Subscribers

Setup Materialize (Streaming SQL DB) with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-Agent MCP Server Provisioning

The `create_cluster` tool lets your infrastructure agent spin up new compute nodes based on real-time demand. A monitoring agent watches load metrics and signals the provisioning agent to deploy an 'l' or 'xl' cluster. They use `list_clusters` to verify the new resources are active. This crew operates entirely on its own, ensuring your streaming pipelines never run out of memory during traffic spikes.

Delegate SQL Execution to Analyst Agents

Your data analyst agent uses the `execute_sql` tool to build continuous views in Materialize. It writes the queries, tests them against the MCP Server, and validates the output. Meanwhile, a reviewer agent checks the query performance. If the joins are too expensive, it kicks the task back to the analyst for optimization before deploying to production.

Always-On Database Health Checks

A dedicated watchdog agent runs `check_health` continuously against your Materialize instance. It sits in the background looking for any signs of instability. When it detects a problem, it immediately escalates. The crew attempts to restart queries or alerts your human on-call rotation without waiting for external monitoring tools to fire.

Setup guide

Set up Materialize (Streaming SQL DB) 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 Materialize (Streaming SQL DB) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Materialize (Streaming SQL DB) 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 Materialize. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Materialize (Streaming SQL DB) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Materialize (Streaming SQL DB) MCP in CrewAI

Pass the server URL directly into your Agent's mcps array. For tighter control, use MCPServerHTTP to filter which tools specific agents can access.

Yes, they operate in parallel. You might have one agent running execute_sql while another checks list_clusters to monitor resource usage.

No custom code is required. The agent inherently knows how to format and send queries using the execute_sql tool provided by the server.

Apply a tool_filter in your configuration. Give your analyst agent access to querying tools while blocking it from calling create_cluster.

Your agents parse live streaming database rows and cluster configurations. Keep your CrewAI deployment within your secure VPC. The server only exposes the specific SQL views you grant it access to.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript