SQL Review Agent

An agentic AI system that reviews BigQuery SQL queries before you run them — catching performance issues, estimating cost, and suggesting rewrites.

Work in progress. Building incrementally — check back for updates.

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What it does

Paste a BigQuery SQL query and the agent:

1. Dry-runs it — gets the estimated bytes scanned (= cost) without executing
2. Reads your schema — understands partition keys, clustering, column types
3. Flags issues — full table scans, missing partition filters, cartesian joins, SELECT *
4. Suggests a rewrite — returns improved SQL with explanation
5. Reports severity — low / medium / high / critical

Input SQL
    │
    ▼
dry_run()          get_schema()
→ bytes scanned    → partition keys, types, row count
    │
    ▼
Issue detection + rewrite suggestion
    │
    ▼
ReviewReport (cost · issues · rewritten SQL · severity)

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Three trigger modes

• CLI — python agent.py --sql "SELECT * FROM ..."
• Web UI — paste SQL in browser, see review results
• MCP — any MCP-compatible client (Claude Code, OpenClaw, Cursor, Zed)

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Stack

• Claude / Gemini — LLM provider (auto-detected, free Gemini supported)
• BigQuery — dry_run for cost estimation, schema introspection
• Flask — web UI
• FastMCP — MCP server
• pytest — test suite

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Setup

git clone https://github.com/ARAVINDHRAJA123/sql-review-agent.git
cd sql-review-agent
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt

export GCP_PROJECT=your-project
export BQ_LOCATION=asia-south1
export GEMINI_API_KEY=your-key   # free: aistudio.google.com
# or: export ANTHROPIC_API_KEY=your-key

gcloud auth application-default login

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Project structure

sql-review-agent/
├── agent.py          ← agentic review loop (coming Day 3)
├── server.py         ← Flask web UI (coming Day 4)
├── mcp_server.py     ← FastMCP server (coming Day 5)
├── tools/
│   ├── bq_tools.py   ← dry_run, schema, metadata (coming Day 2)
│   └── sql_tools.py  ← SQL parsing helpers (coming Day 2)
├── tests/
├── docs/
└── requirements.txt