Amazon Redshift 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 Amazon Redshift as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Amazon Redshift. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Amazon Redshift?"
)
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 Amazon Redshift MCP Server
Connect your Amazon Redshift data warehouse securely to your AI agent utilizing the AWS Redshift Data API. This integration empowers your AI interface to natively run aggregations, explore massive schemas, and retrieve historical executing query logs asynchronously without requiring persistent DB connection pools, JDBC drivers, or complex networking configurations.
LlamaIndex agents combine Amazon Redshift tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through 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
- Execute Asynchronous SQL — Direct the AI to execute standard SQL commands (
execute_sql), including complex SELECT aggregations, table creation (DDL), or data mutation (DML). Since it uses the Data API, long-running queries will process in the background. - Poll & Retrieve Results — Ask the agent to proactively monitor the execution lifecycle (
statement_status) of dispatched query IDs and retrieve the dataset rows (get_results) securely into your terminal upon completion. - Schema & Table Discovery — Understand the database structure dynamically by generating lists of available schemas (
list_schemas) or looking up column metadata metrics for specific tables (describe_table). - Statement Histories — Perform audits assessing previously submitted query structures and track analytical workloads running on your configured cluster (
list_statements).
The Amazon Redshift 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 Amazon Redshift to LlamaIndex via MCP
Follow these steps to integrate the Amazon Redshift 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 Amazon Redshift
Why Use LlamaIndex with the Amazon Redshift MCP Server
LlamaIndex provides unique advantages when paired with Amazon Redshift through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Amazon Redshift tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Amazon Redshift tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Amazon Redshift, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Amazon Redshift tools were called, what data was returned, and how it influenced the final answer
Amazon Redshift + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Amazon Redshift MCP Server delivers measurable value.
Hybrid search: combine Amazon Redshift real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Amazon Redshift 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 Amazon Redshift for fresh data
Analytical workflows: chain Amazon Redshift queries with LlamaIndex's data connectors to build multi-source analytical reports
Amazon Redshift MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Amazon Redshift to LlamaIndex via MCP:
describe_table
Retrieves column metadata for a table
execute_sql
This is an asynchronous operation that returns a statement ID. Executes a SQL statement using the Redshift Data API
get_results
Retrieves the results of a completed SQL statement
list_schemas
Lists all database schemas in Redshift
list_statements
Lists recent SQL statements executed in the cluster
list_tables
Lists all tables in a specific schema
statement_status
Checks the execution status of a SQL statement
Example Prompts for Amazon Redshift in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Amazon Redshift immediately.
"List all active tables present inside the 'reporting_schema' schema."
"Describe the column parameters for 'user_cohorts' in the reporting schema."
"Run a query to fetch the sum of sales amounts where region is 'APAC' from the 'quarterly_revenue' table."
Troubleshooting Amazon Redshift MCP Server with LlamaIndex
Common issues when connecting Amazon Redshift to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAmazon Redshift + LlamaIndex FAQ
Common questions about integrating Amazon Redshift 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 Amazon Redshift 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.
AI-first code editor with integrated LLM-powered coding assistance.
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 Amazon Redshift to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.