ClickHouse (Vector Search) MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ClickHouse (Vector Search) as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to ClickHouse (Vector Search). "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in ClickHouse (Vector Search)?"
)
print(response)
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.
LlamaIndex agents combine ClickHouse (Vector Search) tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the ClickHouse (Vector Search) MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 7 tools from ClickHouse (Vector Search)
Why Use LlamaIndex with the ClickHouse (Vector Search) MCP Server
LlamaIndex provides unique advantages when paired with ClickHouse (Vector Search) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ClickHouse (Vector Search) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ClickHouse (Vector Search) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ClickHouse (Vector Search), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ClickHouse (Vector Search) tools were called, what data was returned, and how it influenced the final answer
ClickHouse (Vector Search) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ClickHouse (Vector Search) MCP Server delivers measurable value.
Hybrid search: combine ClickHouse (Vector Search) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ClickHouse (Vector Search) to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying ClickHouse (Vector Search) for fresh data
Analytical workflows: chain ClickHouse (Vector Search) queries with LlamaIndex's data connectors to build multi-source analytical reports
ClickHouse (Vector Search) MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect ClickHouse (Vector Search) to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting ClickHouse (Vector Search) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpClickHouse (Vector Search) + LlamaIndex FAQ
Common questions about integrating ClickHouse (Vector Search) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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.
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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 LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.