gagarwal304/databridge

DataBridge — The Intelligent Database Layer for AI Agents

One MCP server. Any database. Benchmark-proven.

DataBridge is an open-source MCP server that gives AI agents (Claude, GPT, Gemini, and any MCP-compatible agent) reliable, safe, and intelligent access to heterogeneous databases. It sits between your agent and your data — handling connections, enforcing safety, learning schema, normalizing cross-database joins, and running post-query transforms so the agent gets answers, not raw data engineering problems.

Benchmarked on DataAgentBench (DAB) — the UC Berkeley + Hasura benchmark for real-world data agents across 12 datasets and 4 database systems.

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Why DataBridge Exists

Enterprise data lives across multiple systems simultaneously — PostgreSQL for transactions, MongoDB for documents, DuckDB for analytics, SQLite for local state. Answering a single business question often requires querying all of them together.

Current AI agents fail at this in four specific ways:

1. Silent wrong answers. An agent joins PostgreSQL's integer subscriber_id: 12345 with MongoDB's string "CUST-0012345", gets zero rows, and confidently reports "no results found." No error. No warning. Wrong answer delivered with certainty.

2. No safety layer. Agents given database access can — and do — execute destructive operations. A misunderstood task becomes a DELETE FROM orders with no WHERE clause. Prompt-based safety instructions are insufficient. A deterministic enforcement layer is required.

3. Cold start every session. Every new agent session re-discovers schema from scratch — re-reading table definitions, re-learning join patterns, re-discovering that customer_id in PostgreSQL maps to _id in MongoDB. This wastes tokens, time, and produces inconsistent results.

4. Raw row fetching. Agents pull full tables into context when they should push aggregation to the database. A SELECT * on a 500,000-row ta