Exploring ServiceNow Tables

General-purpose table exploration — schema discovery, data profiling, and table comparison.

Workflows

1. Explore a Table

Get the full picture of any ServiceNow table.

Progress checklist (copy into your response):

- [ ] Get table schema
- [ ] Pull sample records
- [ ] Summarize field types, mandatory fields, reference fields

1. Get the table schema (field definitions, types, mandatory flags):

   get_table_schema(table_name="<table_name>")

1. Pull sample records to see real data:

   list_records(table_name="<table_name>", limit=5)

1. Summarize:

• Total field count and types (string, integer, reference, boolean, etc.)
• Mandatory fields (the ones users must populate)
• Reference fields (links to other tables — these define relationships)
• Choice fields (fields with dropdown values)

2. Compare Tables

Side-by-side schema comparison between two related tables.

Progress checklist:

- [ ] Get schema for table A
- [ ] Get schema for table B
- [ ] Compare field overlap and differences

1. Get schemas for both tables:

   get_table_schema(table_name="<table_a>")
   get_table_schema(table_name="<table_b>")

1. Compare:

• Fields present in both (shared/inherited from a common parent)
• Fields unique to each table
• Differences in field types or mandatory flags
• Reference field targets

3. Find Tables

Discover tables matching a keyword by querying sys_db_object.

Progress checklist:

- [ ] Query sys_db_object for matching tables
- [ ] Get record counts for top matches
- [ ] Summarize results

1. Search for tables by name or label:

   list_records(table_name="sys_db_object", query="nameLIKE<keyword>", fields="name,label,super_class,sys_id", limit=20)

1. For top matches, get a sample record to confirm the table has data:

   list_records(table_name="<matched_table>", limit=1)

1. Summarize: table name, label, parent class, whether it has data.

4. Data Profiling

Sample records and assess data quality for a table.

Progress checklist:

- [ ] Get table schema
- [ ] Pull sample records
- [ ] Check null rates on key fields
- [ ] Identify unused or sparse columns

1. Get the schema to know what fields exist:

   get_table_schema(table_name="<table_name>")

1. Pull a larger sample:

   list_records(table_name="<table_name>", limit=20)

1. For key fields, check how many have empty values:

   list_records(table_name="<table_name>", query="<field_name>ISEMPTY", limit=1, fields="sys_id")

1. Report:

• Fields with high null rates (potential data quality issues)
• Fields that appear unused (always empty or default)
• Reference fields that point to empty/missing records
• Recommendations for data cleanup

Tips

• Use fields parameter to limit returned data: fields="sys_id,name,state".
• The sys_db_object table contains all table definitions. Use it to discover tables by keyword.
• The sys_dictionary table contains field-level metadata. Query it for advanced schema details: list_records(table_name="sys_dictionary", query="name=<table_name>").
• Encoded query cheat sheet: = (equals), LIKE (contains), ISEMPTY (null), ^ (AND), ^OR (OR).