eddytor-querying

name	eddytor-querying
description	Reads, filters, sorts, aggregates, and analyzes data in Eddytor tables using query_rows, aggregate, and execute_sql. Activates for queries, filters, search, counts, sums, averages, group by, SQL, joins, or data exploration — even without the word "query."
license	CC-BY-NC-4.0
metadata	{"author":"eddytor","version":"1.0"}

Querying Data

Tool selection: default to query_rows

Need	Tool
Read with filters, sort, paginate	query_rows (default)
Count, sum, avg, min, max with grouping	aggregate
Joins, subqueries, CTEs, window functions	execute_sql

Use query_rows unless you need something it can't express.

query_rows

{
  "table": "eddytor.cfg_xxx.abc123_products",
  "columns": ["product_id", "name", "price"],
  "filter": "status = 'active' AND price > 50",
  "order_by": "price DESC",
  "limit": 20
}

Filter syntax: standard SQL WHERE (=, !=, LIKE, IN (...), IS NULL, BETWEEN). Order syntax: "price DESC" or "category ASC, price DESC".

aggregate

{
  "table": "eddytor.cfg_xxx.abc123_products",
  "aggregations": ["COUNT(*)", "AVG(price)"],
  "group_by": ["category"],
  "filter": "status != 'deleted'"
}

Do NOT use aggregate to count tables — use list_tables for that.

execute_sql — only when needed

Table names must be backtick-quoted per component:

SELECT p.`category`, COUNT(*) as cnt
FROM `eddytor`.`cfg_xxx`.`abc123_products` p
WHERE p.`status` = 'active'
GROUP BY p.`category`
HAVING COUNT(*) > 5

Gotchas

• Unquoted dots in execute_sql table names cause parse errors. Always backtick-quote each component.
• String comparisons need quotes in filters: "name = 'Widget'". Numeric don't: "price > 50".
• NULL comparisons: use IS NULL / IS NOT NULL, never = NULL.
• Date filtering uses ISO format: "order_date > '2024-01-15'".
• aggregate operates on rows within one table. It cannot count tables or aggregate across tables.

Procedure: exploring unfamiliar data

1. get_table_schema → understand columns and types
2. query_rows with limit: 10 → see sample data
3. profile_table → statistical overview (nulls, distinct counts, ranges)
4. Refine queries based on what you learned

Guidelines

• Select only needed columns — faster than fetching everything
• Set limit: 100 by default — increase only when the user needs more
• Use filters to push work to the engine, not client-side