Redash MCP for AI. Turn raw data into actionable reports in chat.
Works with every AI agent you already use
…and any MCP-compatible client








How this MCP server connects to your AI agent
Redash MCP lets your AI agent interact with your data warehouse using natural language. You can manage, run, and visualize SQL queries and dashboards without opening a browser tab.
It connects raw database output directly to your chat conversation.
What AI agents can do with Redash Automation
Archive dashboard
Moves an existing dashboard into an archived state, removing it from active view.
Archive query
Archived a specified query object so it's no longer available for use.
Create dashboard
Builds and saves an entirely new dashboard container within the Redash environment.
You can initiate a query run on demand and receive the resulting dataset in your chat.
List, create, update, or archive specific SQL code blocks to keep your workspace organized.
Retrieve the structure and details of existing dashboards without manually building them.
Check the status of complex or long-running data tasks to know when results are ready.
Verify the details and connection health of all underlying data sources.
Ask an AI about this
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What AI agents can do with Redash: 17 Tools for Data Analytics
These tools allow you to programmatically interact with every aspect of your Redash instance, from listing all available reports to executing complex data jobs.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Redash on VinkiusArchive Dashboard
Moves an existing dashboard into an archived state, removing it from active view.
Archive Query
Archived a specified query object so it's no longer available for use.
Create Dashboard
Builds and saves an entirely new dashboard container within the Redash environment.
Create Query
Creates a brand-new, blank query object ready for you to write SQL against.
Execute Query
Starts running a specific query, handling parameters if needed, or fetching stored...
Get Cached Query Result
Retrieves the previously saved result for a non-parameterized query ID.
Get Dashboard
Fetches all the specific details and structure of one particular dashboard.
Get Data Source
Returns detailed information about a connected data source, like credentials or type.
Get Job
Checks the current progress and status of any long-running query job.
Get Query Result
Retrieves a complete result set using only its unique result ID number.
Get Query
Fetches the full object details, including the SQL code, for an individual query.
List Dashboards
Returns a list of all dashboards you have access to in Redash.
List Queries
Provides a comprehensive list of every query saved within your account.
Test Data Source
Runs a connection test to ensure the data source is online and accessible.
Update Dashboard
Edits an existing dashboard object, allowing you to modify its structure or widgets.
Update Data Source
Modifies the configuration settings of a data source, such as updating credentials.
Update Query
Makes changes to an existing query object, including adjusting its SQL code or...
Security and governance baked right in.
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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 Redash, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ 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
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Redash. 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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Built on the Model Context Protocol (MCP) for 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 connection provides 17 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Juggling tabs and context switches for every single report., Solved with Vinkius AI Gateway
Today's routine means opening your browser, logging into Redash, finding the right dashboard by its slug, manually selecting filters, clicking 'Run,' waiting 30 seconds for the job to finish, copying the resulting table data, and then pasting it somewhere else. It’s a multi-step process that breaks focus every time.
With this MCP, you just tell your agent what metric or report you need in plain English. The system handles finding the right dashboard, executing the necessary query, waiting for the job to complete, and retrieving the final data set—all without you moving a finger.
Getting full control over your entire query library.
Before this, if you needed to check what reports were available or archive an old one, you had to navigate the Redash UI and click through multiple menus. It was slow, prone to human error, and difficult to track across different project phases.
Now, your agent treats your entire library like a database of knowledge. You can ask it to list all queries, create new ones, or archive old reports instantly. Your data management process just became conversational.
What your AI can actually do with this
This connector brings the power of structured data analysis into your chat window. Instead of logging into Redash, navigating complex menus, or writing boilerplate code just to check a metric, you tell your agent what you need. The system handles running the necessary SQL queries, fetching fresh results, and pulling up dashboard visualizations—all conversationally.
It gives you full control over your data environment. You can ask it to list all existing reports, run a new query using specific parameters, or even check the status of a long-running job that took hours. For visibility into what's available, the entire Vinkius catalog ensures you connect once and get access to this powerful data tool alongside dozens of others.
It turns complex, multi-step reporting into simple back-and-forth conversation.
019ea601-dcb3-707f-9dbe-984413059e4a Here's how it actually works
The bottom line is that it makes querying your business data feel as natural as sending an email.
Subscribe to this MCP and provide your Redash Base URL and API Key.
Direct your AI agent to perform a task, such as 'Show me the revenue for Q3.'
The system executes the necessary queries and presents you with the structured results directly in your chat.
Who is this actually for?
This MCP is essential for anyone who spends too much time context-switching between a chat interface, a dashboard builder, and a database console. It’s perfect for the Data Analyst who needs immediate query results or the BI Lead who just wants to share insights without sending screenshots.
You use this MCP to quickly run iterative SQL tests, checking parameters and fetching fresh data blocks in a conversational flow.
You monitor the status of critical, scheduled reports or share high-level dashboard findings with stakeholders via chat.
You test data source connections and manage query objects programmatically to ensure system integrity without opening a GUI.
What Changes When You Connect
Eliminate context switching. You run queries and check dashboard status without ever leaving your conversational AI agent.
Manage the entire lifecycle of a report, from creating a new query object to running it and archiving it later, all through natural language commands.
Get immediate data visibility by using execute_query to fetch fresh results or get_cached_query_result for quick checks on non-parameterized data.
Maintain an organized workspace. Use tools like list_queries, archive_query, and create_query to keep your report library clean and functional.
Troubleshoot complex reports easily. You can use get_job to monitor long-running tasks, knowing exactly when the final results are ready for viewing.
See it in action
The Analyst Needs a Quick Metric Check
A data analyst needs to check last week's sales figures but doesn't want to rebuild the full dashboard. Instead of opening Redash, they simply ask their agent to execute a query on demand using execute_query and get the specific numbers right away.
The BI Lead Needs Status Confirmation
A business intelligence lead scheduled a massive report overnight. They don't know if it finished or failed. Rather than checking email digests, they ask their agent to check the job status using get_job, getting an instant confirmation of success or failure.
The Engineer Needs Connection Validation
A data engineer is setting up a new client connection and needs to validate the database link. They use their agent to run test_data_source first, confirming connectivity before writing any code.
The Team Needs Report Cleanup
After a project ends, a team member needs to declutter the workspace by removing old reports. They ask their agent to list all queries and then use archive_query on the ones no longer needed.
The honest tradeoffs
Treating it like a search tool
Typing 'What is my revenue for October?' expecting the agent to magically run and show data without knowing the required query ID.
You must specify action. First, use list_queries to find the correct report name or ID, then tell your agent to execute_query with that specific identifier.
Ignoring job status
Simply telling the agent to 'run the quarterly report' and assuming the result appears instantly, only to wait forever for nothing.
Always ask your agent to check the progress first. Use get_job to monitor if the task is running or complete before expecting final results.
Overwriting definitions
Trying to modify a core dashboard by manually deleting widgets in the UI when you just need minor changes.
Use get_dashboard first to see the current structure, then tell your agent to use update_dashboard, specifying exactly which widget needs modification.
When It Fits, When It Doesn't
Use this MCP if your primary bottleneck is accessing structured data or dashboards that live in Redash. If you need to run SQL code blocks, list saved reports, or check job status, this tool is for you. Don't use it if your goal is simply sending text messages or managing contacts; those require a messaging-type connector. Also, don't use it if the data visualization layer lives in a totally different platform (like Tableau); that requires a separate specialized MCP. However, if your current workflow involves 'I need to check this metric from Dashboard X using Query Y,' this is exactly what you need.
Questions you might have
How do I run a query using the Redash MCP? +
You initiate a run by asking your agent to execute the report you need, often referencing its name or ID. The system then handles running the job and presenting the data.
Can I see all my dashboards with the Redash MCP? +
Yes. You can ask your agent to list all available dashboards using list_dashboards, giving you a full overview of what reports exist for your team.
What if a query is running and I need to know the status? (Redash MCP) +
Use the agent to check job status with get_job. This tells you immediately if the query finished, failed, or is still processing.
Does Redash MCP help me keep old queries organized? +
Absolutely. You can use archive_query to remove outdated code from your active list and list_queries to see what you've kept.
Can the MCP handle parameterized reports? +
Yes, the system handles parameters when running queries. You just need to provide the necessary variables in your request so it can fetch accurate, customized data.
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