Clickhouse MCP | Bearer Token Clickhouse connector for AI agents

Tools your analytics agent reaches for on Clickhouse, scoped per user.

CALL ANY TOOL

Run SQL queries, list databases and tables, inspect schemas, and retrieve query execution logs.

clickhouse_list_tables

List tables

List all tables in a database, including column names and types. Supports LIKE pattern filtering.

Parameters

Name

Type

Required

Description

database

string

Required

Name of the database to list tables from

serviceId

string

Required

The unique identifier of the ClickHouse service

string

Optional

Optional SQL LIKE pattern to filter tables by name (e.g., "events_%")

notLike

string

Optional

Optional SQL LIKE pattern to exclude tables by name

clickhouse_get_clickpipe

Get clickpipe

clickhouse_list_databases

List databases

clickhouse_get_organizations

Get organizations

clickhouse_list_clickpipes

List clickpipes

clickhouse_get_service_details

Get service details

clickhouse_list_service_backups

List service backups

clickhouse_get_postgres_metrics

Get postgres metrics

clickhouse_list_postgres_slow_query_patterns

List postgres slow query patterns

clickhouse_get_organization_cost

Get organization cost

clickhouse_get_services_list

Get services list

clickhouse_get_organization_details

Get organization details

clickhouse_run_select_query

Run select query

clickhouse_get_service_backup_details

Get service backup details

clickhouse_run_postgres_select_query

Run postgres select query

clickhouse_get_service_backup_configuration

Get service backup configuration

clickhouse_get_postgres_slow_query_pattern_details

Get postgres slow query pattern details

Build your Agent

Drop the toolkit in, point it at the user, and your analytics agent can use Clickhouse from the first run.

import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { z } from "zod";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["clickhouse"], toolNames: ["ch_query_run", "ch_databases_list", "ch_tables_list"] },
  pageSize: 100,
});

const lcTools = tools.map((t) => new DynamicStructuredTool({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  schema: z.object({}).passthrough(),
  func: async (args) => {
    const { data } = await sk.tools.executeTool({
      toolName: t.tool.definition.name,
      identifier: "user_123",
      params: args,
    });
    return JSON.stringify(data);
  },
}));

const agent = createReactAgent({ llm, tools: lcTools });

import { ScalekitClient } from "@scalekit-sdk/node";
import OpenAI from "openai";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["clickhouse"], toolNames: ["ch_query_run", "ch_databases_list", "ch_tables_list"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  type: "function",
  function: {
    name: t.tool.definition.name,
    description: t.tool.definition.description,
    parameters: t.tool.definition.input_schema,
  },
}));

const resp = await openai.responses.create({
  model: "gpt-4o", input: prompt, tools: llmTools,
});

import { ScalekitClient } from "@scalekit-sdk/node";
import Anthropic from "@anthropic-ai/sdk";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["clickhouse"], toolNames: ["ch_query_run", "ch_databases_list", "ch_tables_list"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  input_schema: t.tool.definition.input_schema,
}));

const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-6", max_tokens: 1024,
  tools: llmTools,
  messages: [{ role: "user", content: prompt }],
});

import { Agent } from "@google/adk/agents";
import {
  MCPToolset, StreamableHTTPConnectionParams,
} from "@google/adk/tools/mcp";

const toolset = new MCPToolset({
  connectionParams: new StreamableHTTPConnectionParams({
    url: "https://mcp.scalekit.com/clickhouse",
    headers: { Authorization: `Bearer ${userScopedToken}` },
  }),
});

const agent = new Agent({
  name: "agent", model: "gemini-2.0-flash",
  tools: await toolset.getTools(),
});

Try these prompts

Paste any prompt into your agent to start using Clickhouse.

Schema & discovery

List all databases.

Show tables in [database].

Copy the prompt

Copied

Get schema for [database.table].

Copy the prompt

Copied

How many rows are in [table]?

Query & analysis

Copy the prompt

Copied

Run: SELECT event_type, count() FROM events GROUP BY event_type.

Copy the prompt

Copied

Daily active users last 30 days.

Copy the prompt

Copied

Top 10 customers by API call volume.

Copy the prompt

Copied

P99 latency by endpoint last week.

Monitoring & ops

Copy the prompt

Copied

Recent slow queries over 5 seconds.

Copy the prompt

Copied

Query log for user [username] today.

Copy the prompt

Copied

Which tables have the most reads?

Copy the prompt

Copied

Storage size by database.

SEE HOW AUTH WORKS

Users authorize Clickhouse once. Their credentials stay vaulted, every call is checked, and every action is logged.

Authorize

Your user connects

ClickHouse

once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access

Who:

user ‘A’

when:

Once per user

access:

Limited to user

Store

Their

ClickHouse

token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection

vault:

encrypted

scope:

per-user

tokens:

auto-refreshed

Resolve

When your agent calls a

ClickHouse

tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs

speed:

~40ms

check:

before every call

seen by:

nobody

Audit

Every

ClickHouse

tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it

history:

90 days

export:

SIEM-ready

logged:

every call

Test other agents

Same per-user auth pattern across other analytics agents and MCP connectors. Working code, live demos, fork what fits.

GTM and RevOps Teams

Revenue forecast commentary

Pulls open pipeline from Salesforce and HubSpot, calculates coverage against quota, flags at-risk stages, posts commentary to Slack, and logs every snapshot to Google Sheets.

GTM and RevOps Teams

Deal intelligence agent

Pulls recent Gong calls, scores deal risk with an LLM, cross-references the record in Attio, and DMs each owner their at-risk deals in Slack. Every read is scoped to that rep's own access.

Engineering Teams

Engineering standup agent

Pulls commits from GitHub and GitLab, tracks issue movement in Jira, and posts a per-engineer standup brief to Slack. Each engineer's activity is read on their own delegated OAuth.

GTM and RevOps Teams

Competitive intelligence briefing agent

Scans Gong calls for competitor mentions, matches each one to its Notion battlecard, and DMs every affected rep a single Slack digest per cycle. Every call runs as the PMM who owns the briefing, never a shared bot.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

01.

Shared tokens break per-user analytics

A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential so attribution, audit, and scope stay accurate.

// shared token
 audit → bot_service_account
 user_filter → broken

 // scalekit
 audit → user_abc
 scope → enforced ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Clickhouse today. Others tomorrow.

“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”

Venu Madhav Kattagoni

Head of Engineering / Von

FAQs

Frequently Asked Questions

Does the agent access Clickhouse as the user or as a shared key?

As the user. Each workspace member authorizes once and Scalekit resolves their credential at request time. Audit logs attribute every action to that user, not a shared service account.

Where is the Clickhouse bearer token stored?

In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Tokens never appear in prompts, logs, or LLM context.

Can I limit what the agent is allowed to do in Clickhouse?

Yes. Pass a tool name filter to listScopedTools so the analytics agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Clickhouse.

What happens when a user revokes Clickhouse access?

The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.

Does the agent inherit ClickHouse user roles and row-level access policies?

Yes. Queries run under the authorizing user's ClickHouse credentials. Role-based access, row-level policies, and database-level grants all apply. The agent cannot read what the user cannot.

On this page

Tool and actions Framework Compatibility Starter Prompt Auth Lifecycle Try Other Agents Why Scalekit