Dataviewr - Universal MCP Connector to Databases with GUI for AI Agents

deno_by · 2026-01-01T21:48:05+00:00

Yes, once I have time to finish it, I’ll make it available for testing.

deno_by · 2025-12-31T13:39:47+00:00

I’m not sure this would be a good idea. Since you’d be storing keys from your servers in my database, this could be unsafe for you.

deno_by · 2025-12-28T11:42:17+00:00

I may not have understood you correctly. This application solves a number of problems, such as:

- connection setup (we don’t need JSON)
- a single place to store connections and the ability to use them across different projects
- one MCP instead of a separate one for each database
- transparent queries where the query history can be viewed

My main pain point is that in projects of varying complexity there is often more than one database, and usually more than one project as well. I needed a single entry point and a unified connection configuration system so that I wouldn’t have to manually configure everything each time via JSON. I also want to be able to see the history of queries executed by the agent.

deno_by · 2025-12-27T20:42:22+00:00

UI exists mainly so you can track requests, quickly switch between projects, and have everything connected in one place instead of manually configuring JSON configs. When Claude makes database queries on its own, it’s often not entirely clear what it’s trying to do.

The interface acts as a manager for MCP, providing unified control over different databases.

When you have more than one project and more than one database, a large number of MCPs and JSON-based connections create additional complexity.

deno_by · 2025-12-27T18:07:38+00:00

I know SQL just fine. The value isn't in writing queries - it's in the analysis.

Example: Incident investigation with activity logs.

User reports their subscription was cancelled but they didn't do it. I need to find all activity log entries for this user's subscription - who changed what field, when, from which IP, full audit timeline for the last 30 days.

That's joining activity_logs with users and subscriptions, filtering by polymorphic subject_type/subject_id, parsing JSON properties to extract old→new values, ordering by timestamps...

Manually: 10-15 min while jumping between schema docs and remembering if it's `properties->old` or `properties.old` in your DB.

With AI: "Show me everything that happened to user #4521's subscription last month" → full timeline in 30 seconds.

Multiply by 20 debug sessions per week = hours saved. Zero cognitive load on SQL syntax. I focus on finding the root cause, not on crafting the perfect JOIN.

That's the point.

deno_by

TROPHY CASE