| name | check-prod |
| description | Query the production Vercel database via the public API to investigate data issues |
| disable-model-invocation | true |
| argument-hint | ["query description"] |
| allowed-tools | Bash, Read |
Check Production Data
Query the production Sai Explorer API to investigate data in the Vercel Postgres database.
Base URL: https://sai-explorer.vercel.app
Use curl -s to fetch JSON, then pipe to python3 -c for parsing/filtering.
User Request
$ARGUMENTS
Available Endpoints
| Endpoint | Params | Description |
|---|
/api/trades | network, limit, offset | All trades (default limit 1000) |
/api/user-trades | network, address | Trades for a specific user |
/api/user-stats | network, address | Aggregate stats for a user (volume, PnL, trade count) |
/api/user-deposits | network, address | Deposits for a specific user |
/api/user-withdraws | network, address | Withdraws for a specific user |
/api/stats | network | Global platform stats |
/api/volume | network | Volume leaderboard |
/api/deposits | network | All deposits |
/api/withdraws | network | All withdraws |
/api/insights | network | PnL insights and analytics |
/api/chart-data | network, type | Chart time series data |
/api/markets | network | Market info |
/api/collateral | network | Collateral breakdown |
/api/tvl-breakdown | network | TVL by vault |
Default network=mainnet for all endpoints.
Key Data Mappings
- Micro-units:
collateralAmount, realizedPnlCollateral, openCollateralAmount are in micro-units (divide by 1,000,000)
- USD conversion:
microAmount / 1e6 * collateralPrice
- Collateral types: USDC (price ~$1.0) or stNIBI (price ~$0.005)
- Trade fields: Nested under
trade.perpBorrowing.collateralToken.symbol and trade.perpBorrowing.baseToken.symbol
- Address formats: Users have both Bech32 (
nibi1...) and EVM (0x...) addresses
Instructions
- Determine which endpoint(s) to query based on the user's request
- Fetch data with
curl -s and parse with python3 -c
- Present findings in a clear, formatted table or summary
- If investigating data quality, check for NULL values, unexpected ranges, or inconsistencies
- For large datasets, use
limit and offset to page through results
