sql-review-agent
Provides integration with Google BigQuery for dry-running SQL queries, estimating costs, and retrieving schema information to review and suggest improvements for BigQuery SQL queries.
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In the chat, type
@followed by the MCP server name and your instructions, e.g., "@sql-review-agentReview query: SELECT * FROM orders"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
SQL Review Agent
An agentic AI system that reviews BigQuery SQL queries before you run them — catching performance issues, estimating cost, and suggesting rewrites.
What it does
Paste a BigQuery SQL query and the agent:
Static analysis — instantly flags
SELECT *, missing partition filters, cartesian joinsSchema fetch — reads partition keys, clustering fields, and row counts from BigQuery
Cost estimate — dry-runs the query to get bytes scanned without executing it
Rewrite — returns improved SQL with a plain-English explanation
Severity rating —
none/low/medium/high/critical
Related MCP server: BigQuery FinOps MCP Server
Architecture
The agent is modelled as a LangGraph state machine:
call_llm — sends messages + tools to Claude/Gemini
run_tools — dispatches
get_table_schema,dry_run_sql,write_reportroute — conditional edge: loop back if tool calls remain, stop when
write_reportis called
Three trigger modes
Mode | How |
CLI |
|
Web UI |
|
MCP | Any MCP-compatible client (Claude Code, Cursor, Zed, OpenClaw) |
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 tier: aistudio.google.com
# or: export ANTHROPIC_API_KEY=your-key
gcloud auth application-default loginMCP (Claude Code)
claude mcp add -s user sql-review -- \
/path/to/venv/bin/python /path/to/mcp_server.pyTools available in any MCP client:
review_sql— full agentic review (LLM + BQ)quick_check— instant static analysis, no LLM needed
CLI usage
python agent.py --sql "SELECT * FROM \`project.dataset.table\`"
python agent.py --file query.sql --verboseWeb UI

python server.py
# open http://localhost:5001Tests
pytest
pytest tests/test_tools.py -vProject structure
sql-review-agent/
├── agent.py ← raw tool-use loop (Claude + Gemini)
├── graph_agent.py ← LangGraph state machine (drop-in replacement)
├── mcp_server.py ← FastMCP server (review_sql + quick_check tools)
├── server.py ← Flask web UI + JSON API
├── tools/
│ ├── bq_tools.py ← dry_run, schema, metadata, read-only guard
│ └── sql_tools.py ← static analysis, table extraction
├── tests/
│ ├── test_tools.py ← 24 unit tests
│ └── test_agent.py ← 7 unit tests
└── docs/
├── architecture_graph.svg ← state machine diagram
└── web_ui.png ← Flask UI screenshotStack
Python · BigQuery · Claude API · Gemini API · LangGraph · FastMCP · Flask · pytest
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