Redash MCP — Dashboard Data Without the SQL
Improvado MCP extracts data from Redash and makes it queryable by AI agents. Ask about query results, dashboard metrics, and data freshness without writing SQL or opening Redash.
46K+ metrics · Read & Write access · 500+ platforms · <60s setup
Query Redash Reports and Data Without SQL
Improvado MCP connects Redash query results and dashboard data to AI, so teams can ask questions about any metric without writing a query or navigating the Redash interface.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
What does the weekly revenue dashboard show for this month?
30 min → 30 sec
Show me the results of the user funnel query
Manual → auto
Which queries are returning zero results today?
1 hr → 1 min
Example prompts
"Show anomalies across all accounts"
2h → 40s
"CPL in New York vs. California?"
1h → 30s
"ROAS by campaign type, last 30 days"
45m → 15s
Works with
Claude
ChatGPT
Cursor
+5
Update the date range in the Q1 performance query
1 hr → 2 min
Add a filter to exclude internal traffic from the acquisition dashboard
Manual → auto
Schedule the weekly report query to run every Monday
45 min → 3 min
Write actions
"Launch A/B test, $5K budget"
5 days → 20m
"Shift 20% of Display to PMax"
2h → 1m
"Pause all ad groups with CPA > $50"
30m → 10s
🛡
Every action logged · Fully reversible · SOC 2 certified
Update Queries and Dashboards Through AI
Modify query parameters, refresh dashboards, and update data source configurations directly from your AI agent — without navigating Redash manually.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
Keep Queries Running and Data Fresh
Monitor query execution failures, stale dashboard data, and data source connectivity continuously — your AI agent surfaces broken queries before they reach stakeholders.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
Alert me if any scheduled query fails to execute
Manual check → auto
List all dashboards with data older than 24 hours
Manual → auto
Track query execution times for performance regressions
Weekly review → instant
Monitor prompts
"Flag ad groups over 120% budget"
3h → 1m
"Weekly report: spend, CPA, anomalies"
3h → auto
"Which creatives are fatiguing?"
2h → 30s
Alerts sent to Slack, email, or your AI agent
One conversation. All six phases. Every platform.
The Closed Loop: Read → Decide → Write → Monitor
Modify query parameters, refresh dashboards, and update data source configurations directly from your AI agent — without navigating Redash manually.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
SQL Dependency for Every Question
THE PROBLEM
Getting answers from Redash requires writing or finding the right SQL query — blocking non-technical stakeholders from accessing data independently.
HOW MCP SOLVES IT
Improvado MCP makes Redash query results and dashboard data available to AI agents, so anyone can get answers through plain-language questions.
What are the top acquisition channels from last week's report?
Try asking
"Show ROAS across all 120 accounts"
⚡
Answer in seconds
All data sources, one query
Which scheduled queries have failed in the last 12 hours?
Try asking
"What's my CPL in New York vs. California?"
🔍
Full detail preserved
No data loss on export
Silent Query Failures
THE PROBLEM
Scheduled queries fail silently, and stakeholders receive stale or empty dashboard data without knowing anything is wrong.
HOW MCP SOLVES IT
AI agents monitor query execution status continuously and alert teams the moment a scheduled query fails — before stale data reaches anyone.
Duplicated Query Sprawl
THE PROBLEM
Redash environments accumulate duplicate and abandoned queries over time, making it hard to know which data is authoritative.
HOW MCP SOLVES IT
AI agents audit the query library, surface duplicates, and identify abandoned queries — enabling teams to maintain a clean, trusted data environment.
Find all queries that appear to measure the same metric
Try asking
"PMax vs. Search ROAS for Q1?"
⚖️
Unified data model
Compare anything side by side
Agency CEO
Portfolio health. Client risk. Revenue signals.
Media Strategist
70% strategy, not 70% ops. Auto campaign QA.
Marketing Analyst
Zero wrangling. Cross-platform. AI narratives.
Account Manager
QBR decks auto-generated. Call prep in 30s.
Creative Director
Performance-to-brief. Predict winners before spend.
👥 Teams
One Framework. Five Roles. Zero Setup.
Same MCP connection, different workflows for every team member. Agency CEOs get portfolio health. Media Strategists get campaign QA. Analysts get cross-platform reports. Account Managers get auto-generated QBR decks. Creative Directors get performance-based briefs.
Each role asks in natural language. The MCP server handles the complexity — rate limits, auth, schema normalization, governance — behind the scenes.
Frequently Asked Questions
Redash MCP is an integration that connects Redash query results, dashboard data, and data source metadata to AI agents via the Improvado MCP server. Teams can query BI data and manage reporting infrastructure using plain-language prompts.
What data does Improvado extract from Redash?
+
Improvado extracts query results, dashboard configurations, data source connections, scheduled query history, and execution metadata from Redash.
Can AI agents run or modify Redash queries through MCP?
+
Yes. AI agents can trigger query execution, update query parameters, and modify scheduling configurations directly through Improvado MCP — without accessing the Redash UI.
Do I still need SQL knowledge to use Redash through MCP?
+
No. Improvado MCP abstracts the SQL layer — teams interact with Redash data through plain-language prompts in their AI agent, without writing or modifying queries directly.
How does Redash MCP help with data reliability?
+
AI agents continuously monitor query execution health, flag failures, and track data freshness — reducing the risk of stale or broken data reaching stakeholders.
Which AI agents are compatible with Redash MCP?
+
Any MCP-compatible AI agent works with Improvado MCP, including Claude, enterprise AI platforms, and custom LLM pipelines built on the Model Context Protocol.
What is Redash MCP?
Redash MCP is an integration that connects Redash query results, dashboard data, and data source metadata to AI agents via the Improvado MCP server. Teams can query BI data and manage reporting infrastructure using plain-language prompts.
What data does Improvado extract from Redash?
Improvado extracts query results, dashboard configurations, data source connections, scheduled query history, and execution metadata from Redash.
Can AI agents run or modify Redash queries through MCP?
Yes. AI agents can trigger query execution, update query parameters, and modify scheduling configurations directly through Improvado MCP — without accessing the Redash UI.
Do I still need SQL knowledge to use Redash through MCP?
No. Improvado MCP abstracts the SQL layer — teams interact with Redash data through plain-language prompts in their AI agent, without writing or modifying queries directly.
How does Redash MCP help with data reliability?
AI agents continuously monitor query execution health, flag failures, and track data freshness — reducing the risk of stale or broken data reaching stakeholders.
Which AI agents are compatible with Redash MCP?
Any MCP-compatible AI agent works with Improvado MCP, including Claude, enterprise AI platforms, and custom LLM pipelines built on the Model Context Protocol.
Stop Reporting. Start Executing.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
SOC 2 Type II
GDPR
500+ Platforms
46K+ Metrics
.png){kind=logo}
.png)
