AWS Redshift connector for AI agents

CALL ANY TOOL

7 tools covering describe, execute, cancel.

redshift_cancel_query

Cancel a running amazon redshift sql statement using its sta

Cancel a running amazon redshift sql statement using its statement id.

Parameters

Name

Type

Required

Description

statement_id

string

Required

The ID of the statement to cancel. Must be non-empty.

redshift_describe_table

Describe the schema of a table in amazon redshift using the

redshift_execute_sql

Execute a sql statement against amazon redshift using the re

redshift_get_query_result

Retrieve the results of a previously executed redshift sql s

redshift_list_schemas

List schemas in an amazon redshift database using the redshi

redshift_list_statements

List previously executed sql statements in amazon redshift u

redshift_list_tables

List tables in an amazon redshift database using the redshif

Build your Agent

Same auth pattern across every framework.

from langchain_mcp_adapters.client import MultiServerMCPClient
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="redshift")

mcp = MultiServerMCPClient({
    "redshift": {
        "url": "https://mcp.scalekit.com/redshift",
        "headers": {"Authorization": "Bearer " + token}
    }
})
tools = await mcp.get_tools()

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

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "redshift" });

const openai = new OpenAI();
// Connect to MCP at https://mcp.scalekit.com/redshift
// Pass: Authorization: Bearer + token

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

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "redshift" });

const anthropic = new Anthropic();
// Connect to MCP at https://mcp.scalekit.com/redshift
// Pass: Authorization: Bearer + token

from google.adk.agents import LlmAgent
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="redshift")
# Connect to MCP at https://mcp.scalekit.com/redshift
# Pass: Authorization: Bearer + token

Try these prompts

Paste any prompt into your agent to get started.

Get started

Copy the prompt

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List tables in an Amazon Redshift database using the Redshift Data API?

Copy the prompt

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Retrieve the results of a previously executed Redshift SQL statement using the statement ID returned by redshift_execute?

Advanced

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Execute a SQL statement against Amazon Redshift using the Redshift Data API?

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Describe the schema of a table in Amazon Redshift using the Redshift Data API, including column names, types, and metada?

SEE HOW AUTH WORKS

User authorises once. Every agent call after uses their token with scope enforcement.

1

Authorize

Your user connects

AWS Redshift

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

2

Store

Their

AWS Redshift

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

3

Resolve

When your agent calls a

AWS Redshift

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

4

Audit

Every

AWS Redshift

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

See the same per-user auth pattern across other connectors.

SALES

Deal intelligence agent

Combine Gong, Attio, and Slack signals to surface deal risks and next-best actions. Updated after every call.

ENGINEERING

Engineering standup agent

Aggregate GitHub and GitLab activity, link to Jira, and post a daily standup digest to Slack. No async updates.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought.

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.

// shared token
 audit → bot_service_account

 // scalekit
 audit → user_abc ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

One connector today. Ten 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 AWS Redshift 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 AWS Redshift 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 AWS Redshift?
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 AWS Redshift.

What happens when a user revokes AWS Redshift 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 SQL execution respect Redshift grants?
Yes. execute_sql and schema inspection run under the connected Redshift credential's grants, so schema and table permissions hold. Scalekit vaults the credential and scope-checks every statement pre-call.

AWS Redshift

AWS Redshift tools for AI agents