Can my agent perform high-speed vector similarity searches?

Yes. Provide the database, table, and the vector embedding array in JSON format. The agent uses ClickHouse's native distance functions (cosine or L2) to return the closest matches, leveraging ClickHouse's industry-leading OLAP performance.

Can I execute arbitrary SQL commands directly through the agent?

Absolutely. The 'execute_sql' tool allows you to push any valid ClickHouse SQL (DML, DDL, or SELECT) to your cluster. This is perfect for managing tables, updating records, or generating custom analytical reports on the fly.

How do I check if my ClickHouse instance supports HNSW indices?

Ask your agent to get the version details. The agent checks your ClickHouse build and identifies exactly which capability branches are active, confirming if advanced vector features like HNSW support are available in your runtime environment.

ClickHouse (Vector Search) MCP Server for CrewAI 7 tools — connect in under 2 minutes

Name: ClickHouse (Vector Search)
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 7 Tools Framework

Connect your CrewAI agents to ClickHouse (Vector Search) through the Vinkius — pass the Edge URL in the `mcps` parameter and every ClickHouse (Vector Search) 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

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="ClickHouse (Vector Search) Specialist",
    goal="Help users interact with ClickHouse (Vector Search) effectively",
    backstory=(
        "You are an expert at leveraging ClickHouse (Vector Search) 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 ClickHouse (Vector Search) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 7 available tools "
        "and what they can do."
    ),
)

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

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About ClickHouse (Vector Search) MCP Server

Connect your ClickHouse cluster to any AI agent and take full control of your analytical and vector data through natural conversation.

When paired with CrewAI, ClickHouse (Vector Search) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call ClickHouse (Vector Search) 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

Schema Management — List databases and tables, and inspect deep column schemas including specialized Array(Float32) vector types
SQL Execution — Push arbitrary DML, DDL, or SELECT queries to your cluster to manage data and generate real-time reports
Vector Search — Identify mathematical distance traces using cosineDistance or L2Distance metrics for high-dimensional semantic search
Cluster Monitoring — Extract internal structural states, row counts, and compression ratios to audit cluster health
Capability Auditing — Check instance versions and binary limits to identify exact capability branches like HNSW support

The ClickHouse (Vector Search) MCP Server exposes 7 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 ClickHouse (Vector Search) to CrewAI via MCP

Follow these steps to integrate the ClickHouse (Vector Search) 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 7 tools from ClickHouse (Vector Search)

Why Use CrewAI with the ClickHouse (Vector Search) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with ClickHouse (Vector Search) 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

ClickHouse (Vector Search) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the ClickHouse (Vector Search) MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries ClickHouse (Vector Search) 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 ClickHouse (Vector Search), analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain ClickHouse (Vector Search) 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 ClickHouse (Vector Search) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

ClickHouse (Vector Search) MCP Tools for CrewAI (7)

These 7 tools become available when you connect ClickHouse (Vector Search) to CrewAI via MCP:

describe_table

Perform structural extraction of properties driving active column schemas

execute_sql

Provision a highly-available SQL execution pushing arbitrary arbitrary DML/DDL or SELECTs

get_table_stats

Extracts explicitly attached internal structural states pulling cluster health

get_version

g. HNSW support). Identify precise active cluster limits spanning the execution runtime

list_databases

Identify bounded logical arrays managing top-level ClickHouse schemas

list_tables

Retrieve the exact structural matching verifying table limits inside a database

vector_search

Identify explicit mathematical distance traces routing Vector Embeddings

Example Prompts for ClickHouse (Vector Search) in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with ClickHouse (Vector Search) immediately.

"List all databases in my ClickHouse cluster"

"Find the top 5 most similar records in table 'embeddings' using this vector: [0.1, 0.5, -0.2]"

"Get table stats for 'analytics_prod.sales_data'"

Troubleshooting ClickHouse (Vector Search) MCP Server with CrewAI

Common issues when connecting ClickHouse (Vector Search) 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.

ClickHouse (Vector Search) + CrewAI FAQ

Common questions about integrating ClickHouse (Vector Search) 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.