Amazon Redshift MCP. Run petabyte-scale SQL queries from your chat.
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Amazon Redshift MCP Server lets your AI client talk directly to your petabyte-scale data warehouse. Use the serverless AWS Redshift Data API to run complex SQL queries, check table schemas, and audit execution logs without needing dedicated JDBC drivers or persistent connections.
It lets you treat your massive data lake like a simple chat conversation.
What your AI agents can do
Describe table
Gets column metadata for a specified table.
Execute sql
Runs a SQL statement asynchronously, returning a unique statement ID.
Get results
Retrieves the actual data rows for a completed SQL statement.
The agent lists all available schemas or provides detailed column metadata for any specific table.
The agent runs standard SQL commands, handling complex queries like SELECT aggregations or DML operations asynchronously.
The agent tracks the execution status of a query ID, letting you know when a long-running job is done.
The agent fetches the final data rows from a completed SQL query and displays them to you.
The agent lists recent SQL statements run on the cluster, helping you track past activity.
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Supported MCP Clients
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Amazon Redshift MCP Server: 7 Tools for Data Querying
These tools let your AI client interact with your Redshift data warehouse by discovering schemas, running SQL, and retrieving results.
019d75fddescribe table
Gets column metadata for a specified table.
019d75fdexecute sql
Runs a SQL statement asynchronously, returning a unique statement ID.
019d75fdget results
Retrieves the actual data rows for a completed SQL statement.
019d75fdlist schemas
Lists every schema available in the Redshift cluster.
019d75fdlist statements
Lists the most recent SQL queries run on the cluster.
019d75fdlist tables
Lists all tables within a specific schema.
019d75fdstatement status
Checks if a previously executed SQL statement is still running or if it finished.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Amazon Redshift, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Your AI client talks directly to your petabyte-scale data warehouse using the Amazon Redshift MCP Server. You use the serverless AWS Redshift Data API to run complex SQL queries, check table schemas, and audit execution logs without needing dedicated JDBC drivers or persistent connections. It lets you treat your massive data lake like a simple chat conversation.
Discover Schema Structure
To figure out what data you're working with, your agent can list every schema available in the Redshift cluster using list_schemas, or show you all tables inside a specific schema with list_tables. If you need to know what columns a table has, describe_table gets that column metadata for any table you name.
Execute Background SQL
When you tell your AI to run a query, it uses execute_sql to send the SQL statement. This runs the query asynchronously, giving you a unique statement ID for tracking. This handles standard SQL commands, including complex SELECT aggregations, data definition language (DDL) changes, and data manipulation language (DML) operations.
Monitor Query Status
If your query is going to take a minute or two, you don't want to wait on the terminal. You can track the job's status using statement_status and feed it a query ID. This tells you if the statement is still running or if it's finished.
Retrieve Query Results
Once the query is done, your agent fetches the final data rows using get_results and displays them right in the chat. It's straightforward: run the query, wait for the status check, then grab the results.
Audit Statements
You can keep track of what's been run on the cluster by listing the most recent SQL statements using list_statements. You can also check the overall structure by listing all schemas with list_schemas.
This setup lets your AI client act like a data analyst on your end, running ad-hoc aggregations and checking metrics instantly without opening an external SQL IDE. Data engineers use it to audit Redshift cluster loads and verify execution lifecycles for large reporting workloads directly through their conversational toolkit. Data scientists run complex queries and pull dataset metrics and schemas instantly into the chat.
How Amazon Redshift MCP Works
- 1 Authorize the Amazon Redshift MCP plugin and configure the serverless integration using your AWS credentials (Access Key ID & Secret).
- 2 Prompt your AI client with a query or request (e.g., 'List all tables in the public schema').
- 3 The agent uses the appropriate tool (
list_tables,execute_sql, etc.) and returns the results or status directly into your chat.
The bottom line is you get to run complex data warehouse commands using natural language, without ever leaving your chat interface.
Who Is Amazon Redshift MCP For?
This is for the Data Analyst who needs to run ad-hoc queries without switching to DBeaver. It's for the Backend Developer who needs to test schema migrations interactively. It's for the Data Engineer who needs to audit cluster loads and verify long-running query lifecycles.
Runs exploratory aggregations and pulls dataset metrics using natural language prompts, avoiding the need to manually write complex SQL.
Tests schema changes and checks data integrity by running live queries and inspecting table metadata directly within the code editor.
Audits cluster activity and verifies the execution status of large reporting workloads, keeping track of resources and query history.
What Changes When You Connect
- Run complex data queries without leaving your interface. Use
execute_sqlto submit any standard SQL, and the agent handles the background processing. - See the full schema structure instantly. Use
describe_tableto check column types and constraints for any table without looking up documentation. - Track long-running jobs. If a query takes minutes, use
statement_statusto monitor its progress and know exactly when to callget_results. - Understand the data layout quickly. Start with
list_schemasto see the top-level groupings, then uselist_tablesto see the tables inside. - Audit the cluster activity. Use
list_statementsto review a history of every SQL command run, which is key for compliance and debugging.
Real-World Use Cases
Investigating a Data Discrepancy
A Data Analyst notices a revenue number is off. They ask their agent to check the 'quarterly_revenue' table. The agent uses describe_table first to confirm the date columns, then uses execute_sql to run a specific aggregate query, and finally uses get_results to pull the corrected number into the chat.
Debugging a Pipeline Failure
A Data Engineer's pipeline fails. Instead of checking external logs, they ask the agent to run list_statements to see the last few queries. They see a suspicious ID and use statement_status to confirm if the job is still stuck, fixing the issue without leaving their terminal.
Schema Discovery on a New Project
A Backend Developer joins a new project. They ask the agent to map the data. The agent uses list_schemas to find the relevant area, then list_tables to narrow it down, and finally describe_table to get the column names needed for coding.
Running Ad-Hoc Aggregations
A Scientist needs to count all user signups yesterday. They prompt the agent with the SQL count. The agent uses execute_sql to submit the job, monitors it with statement_status, and uses get_results to deliver the final count to the chat.
The Tradeoffs
Over-relying on single calls
Just asking the agent to 'Give me the revenue data.' This is too vague. The agent doesn't know which schema or which specific metrics you need, leading to a generic failure or asking for more input anyway.
→
Always narrow it down. Start by asking the agent to use list_schemas to find the right data domain, then use list_tables to pinpoint the exact table, and finally use describe_table to confirm the necessary column names before running execute_sql.
Ignoring query state
Running a massive query with execute_sql and assuming the results will pop up immediately. The query will run in the background for minutes, and you'll get a timeout error.
→
Remember that execute_sql only submits the job. You must check the status using statement_status periodically. Only call get_results once statement_status confirms the job is complete.
Mixing up metadata and action
Trying to use describe_table to find the results of a query, or using list_schemas to run a calculation. These tools have separate jobs.
→
Metadata tools are for discovery: use list_schemas or describe_table. Action tools are for data: use execute_sql (to run) and get_results (to get the output).
When It Fits, When It Doesn't
Use this if your job involves running complex, multi-step data queries against a massive data warehouse. You need to audit history, check schemas, and manage the state of long-running jobs. Don't use this if you only need to view a simple, static dashboard or if you're working with small, relational data sets that fit in memory. If you only need to run simple SELECT statements on tiny tables, a simpler database connector might be enough. But for petabyte-scale data, this is the only way to get the full picture without leaving your workflow.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Amazon Redshift. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 7 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
The old way of querying data means jumping between five different apps.
Today, running a query means opening your SQL IDE (DBeaver, etc.), manually writing the query, running it, then switching to a separate dashboard tool to visualize the results. If the query is complex, you have to manually check the execution logs in a third place. It's a copy-paste mess.
With the Amazon Redshift MCP Server, you talk to your AI agent. You ask the question in plain language. The agent handles the connection, the query submission (`execute_sql`), the background wait, and finally, it brings the result right back into your chat. No switching apps.
Amazon Redshift MCP Server: Run complex queries from chat.
The biggest time sink is the data discovery phase. You waste time opening the database explorer, clicking schema by schema, and running `describe_table` just to confirm a column name. You also waste time checking if a massive query is still running.
The MCP Server handles all that. You just tell your AI to 'Describe the columns for the user table.' It runs `describe_table` and shows you the output immediately. The entire process is contained, fast, and actionable.
Common Questions About Amazon Redshift MCP
How do I use the `execute_sql` tool with Amazon Redshift MCP Server? +
The agent submits the query via execute_sql and returns a unique ID. You must use that ID with statement_status to check if the query is done before calling get_results.
Can I list all the available schemas using `list_schemas`? +
Yes, list_schemas lists every schema in the Redshift cluster. This is the first step if you don't know where your required tables live.
What is the difference between `list_tables` and `list_schemas`? +
list_schemas lists the top-level containers (schemas). list_tables lists the actual tables that exist inside a specific schema.
If my query fails, how do I find out why using Amazon Redshift MCP Server? +
You can use list_statements to see the history of recent queries. This helps you see the structure of the query that failed and diagnose the issue.
Does `get_results` work if the query failed? +
No. get_results only retrieves data from a query that has successfully completed. You must verify the status with statement_status first.
How do I check if a long-running query is finished using `statement_status`? +
You check the status using statement_status(statement_id). This function returns the current state (e.g., 'SUCCEEDED', 'FAILED', 'RUNNING') of the query ID you passed in. You keep calling it until the status is no longer 'RUNNING' to know when to fetch the data.
What information does `describe_table` provide about a specific table? +
describe_table returns the full metadata for a table, including column names, data types (like integer or varchar), and nullability constraints. This lets you confirm the exact structure before writing any SQL.
Does `get_results` require me to manually know the query ID? +
Yes, get_results requires the ID of the query that finished. You must first execute the query using execute_sql and then wait for the statement_status to confirm success. You pass the resulting ID into get_results.
Are query results limited by size? +
Yes. The underlying Redshift Data API imposes soft constraints; for enormous responses, you might receive a paginated NextToken. While this MCP server auto-handles some response collection, queries returning over a few megabytes of raw JSON should be pre-filtered using LIMIT or aggregated to avoid token constraints in the LLM.
Can I use standard IAM credentials or do I need specific AWS roles? +
The integration accepts standard static IAM keys (AWS_ACCESS_KEY_ID & AWS_SECRET_ACCESS_KEY), provided they hold sufficient IAM inline or attached policies allowing use of redshift-data:* operations targeting your exact Cluster ARN.
Why does `execute_sql` only return a statement ID instead of the data? +
Because the Amazon Redshift Data API is strictly asynchronous. Queries often take seconds to minutes. Returning the statement_id instantly allows the AI to continue parsing conversations or interacting with other systems without locking up, executing get_results at a later time when the query officially succeeds.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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