How do I find my StarRocks FE Host and Port?

The FE Host is the IP address or domain of your StarRocks Frontend node. The default HTTP port is 8030. You can verify these in your deployment configuration or by contacting your administrator.

Can I execute DELETE or UPDATE queries through this server?

Yes. Use the `execute_query` tool. As long as your authenticated user has the necessary permissions in StarRocks, you can execute any valid SQL statement, including DDL and DML operations.

Is it possible to monitor cluster health through the agent?

Yes! Use the `get_cluster_info` and `list_nodes` tools to retrieve real-time status for all Frontend and Backend nodes, helping you identify offline or overloaded nodes instantly.

StarRocks MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: StarRocks
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to StarRocks through the Vinkius — pass the Edge URL in the `mcps` parameter and every StarRocks tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="StarRocks Specialist",
    goal="Help users interact with StarRocks effectively",
    backstory=(
        "You are an expert at leveraging StarRocks tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in StarRocks "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About StarRocks MCP Server

Empower your AI agent to orchestrate your high-performance OLAP infrastructure with StarRocks, the leading distributed analytical database. By connecting StarRocks to your agent, you transform complex cluster auditing, schema management, and data querying into a natural conversation. Your agent can instantly list databases, retrieve table schemas, monitor backend nodes, and even execute complex SQL queries without you ever needing to open a SQL terminal or the StarRocks Manager. Whether you are conducting a data audit or monitoring real-time ingestion jobs, your agent acts as a real-time data reliability assistant, keeping your analytical platform accurate and your insights moving.

When paired with CrewAI, StarRocks becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call StarRocks tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

What you can do

Database Orchestration — List all databases and retrieve detailed table schemas and structures.
Analytical Querying — Execute arbitrary SQL queries directly through the agent to retrieve real-time insights.
Cluster Monitoring — Browse status and metadata for Frontend (FE) and Backend (BE) nodes to audit health.
Ingestion Control — Monitor data load jobs and historical ingestion performance for your analytical pipelines.
Storage Insights — Retrieve disk usage and data size statistics across the entire distributed cluster.

The StarRocks MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect StarRocks to CrewAI via MCP

Follow these steps to integrate the StarRocks MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 10 tools from StarRocks

Why Use CrewAI with the StarRocks MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with StarRocks through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

StarRocks + CrewAI Use Cases

Practical scenarios where CrewAI combined with the StarRocks MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries StarRocks for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries StarRocks, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain StarRocks tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries StarRocks against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

StarRocks MCP Tools for CrewAI (10)

These 10 tools become available when you connect StarRocks to CrewAI via MCP:

execute_query

Execute arbitrary SQL query

get_cluster_info

Get frontend nodes info

get_storage_usage

Get data storage statistics

get_table_schema

Get table structure

list_databases

List all databases

list_jobs

List data load jobs

list_mvs

List materialized views

list_nodes

List backend nodes

list_tables

List tables in a database

list_views

List database views

Example Prompts for StarRocks in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with StarRocks immediately.

"List all databases in my StarRocks cluster."

"Show me the average order value from the 'sales' table."

"Check for any offline backend nodes."

Troubleshooting StarRocks MCP Server with CrewAI

Common issues when connecting StarRocks to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

StarRocks + CrewAI FAQ

Common questions about integrating StarRocks MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.