Every data-driven team has this problem.
A PM asks: "What's our churn rate this month vs last?"
You write a SQL query. Run it. Format it. Paste it into Slack.
Next week, same question. Different Slack thread. Same SQL.
It's not hard work — it's repetitive work. And it's eating engineering time that should go toward building.
MCP changes this.
The Model Context Protocol (MCP) lets AI models like Claude talk directly to external systems — including your databases. Instead of you writing the query, the AI writes it, runs it, and returns the answer.
The problem? Setting up MCP database connections from scratch involves a lot of moving parts. Auth, schema discovery, query routing, permissions.
That's what Conexor.io handles.
How it works
- Connect your database (PostgreSQL, MySQL, SQL Server, REST API)
- Conexor maps your schema automatically
- Point your MCP client (Claude, Cursor, n8n, Continue…) at your Conexor endpoint
- Ask questions in plain English — get answers from live data
No custom code. No infra to manage. Setup takes ~5 minutes.
Real example
Instead of this:
SELECT
DATE_TRUNC('month', created_at) as month,
COUNT(*) as new_customers,
SUM(mrr) as mrr
FROM subscriptions
WHERE created_at >= NOW() - INTERVAL '2 months'
GROUP BY 1
ORDER BY 1;
You ask Claude: "What's our MRR this month vs last month?"
And you get: $284,500 (+12.3%)
Same data. Zero SQL. Directly in your AI client.
Who it's for
- Engineering teams building AI features with MCP
- Data teams who want to give non-technical stakeholders direct data access
- SaaS companies building AI-native query experiences for customers
Free plan available. 14-day trial, no credit card required.
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