ClickHouse (Vector Search) MCP Server for LangChain 7 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect ClickHouse (Vector Search) through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"clickhouse-vector-search": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using ClickHouse (Vector Search), show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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 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.
LangChain's ecosystem of 500+ components combines seamlessly with ClickHouse (Vector Search) through native MCP adapters. Connect 7 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the ClickHouse (Vector Search) MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 7 tools from ClickHouse (Vector Search) via MCP
Why Use LangChain with the ClickHouse (Vector Search) MCP Server
LangChain provides unique advantages when paired with ClickHouse (Vector Search) through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine ClickHouse (Vector Search) MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across ClickHouse (Vector Search) queries for multi-turn workflows
ClickHouse (Vector Search) + LangChain Use Cases
Practical scenarios where LangChain combined with the ClickHouse (Vector Search) MCP Server delivers measurable value.
RAG with live data: combine ClickHouse (Vector Search) tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query ClickHouse (Vector Search), synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain ClickHouse (Vector Search) tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every ClickHouse (Vector Search) tool call, measure latency, and optimize your agent's performance
ClickHouse (Vector Search) MCP Tools for LangChain (7)
These 7 tools become available when you connect ClickHouse (Vector Search) to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting ClickHouse (Vector Search) to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersClickHouse (Vector Search) + LangChain FAQ
Common questions about integrating ClickHouse (Vector Search) MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect ClickHouse (Vector Search) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect ClickHouse (Vector Search) to LangChain
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.