ER Flow
Click to design your database. AI to build. MCP to connect.
What it does
Build your database schema visually — add tables with a click, define typed columns inline, and drag to create relationships. No other tool makes it this fast. ERFlow's MCP Server connects to AI coding tools like Cursor and Claude Code, letting AI create and modify your schema in real-time. Connect directly to PostgreSQL, MySQL, or SQLite to run queries inside the modeling tool. Includes real-time collaboration, migration generation, and schema versioning. Free plan available.
Does the same job
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MCP-Builder.ai11d ago · mcp-builder.ai · ▲174The fastest way to connect your data with your AI Tools.
- CRCloud-Ready Postgres MCP Server2025 · github.com · ▲167
Hey HN, I built pg-mcp, a Model Context Protocol (MCP) server for PostgreSQL that provides structured schema inspection and query execution for LLMs and agents. It's multi-tenant and runs over HTTP/SSE (not stdio) Features - Supports multiple database connections from multiple agents - Schema Introspection: Returns table structures, types, indexes and constraints; enriched with descriptions from pg_catalog. (for well documented databases) - Read-Only Queries: Controlled execution of queries via MCP. - EXPLAIN Tool: Helps smart agents optimize queries before execution. - Extension…
- CAChartDB Agent – Cursor for DB schema designOct 2025 · app.chartdb.io · ▲130
Last year we launched ChartDB OSS (https://news.ycombinator.com/item?id=44972238) - an open-source tool that generates ER diagrams from your database (via query/sql/dbml) without needing direct DB access. Now we’re launching the ChartDB Agent. It helps you design databases from scratch or make schema changes with natural language. You can: - Generate schemas by simply describing them in plain English - Brainstorm new tables, columns, and relationships with AI - Iterate visually in a diagram (ERD) - Deterministically export SQL script Try it out here -…
- IBI built a database for AI agentsApr 2026 · github.com · ▲12
Hey HN, I just spent the last few weeks building a database for agents. Over the last year I built PostHog AI, the company's business analyst agent, where we experimented on giving raw SQL access to PostHog databases vs. exposing tools/MCPs. Needless to say, SQL wins. I left PostHog 3 weeks ago to work on side-projects. I wanted to experiment more with SQL+agents. I built an MVP exposing business data through DuckDB + annotated schemas, and ran a benchmark with 11 LLMs (from Kimi 2.5 to Claude Opus 4.6) answering business questions with either 1) per-source MCP access (e.g. one Stripe…
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