record-operations

name	record-operations
description	Record CRUD, filtering, sorting, bulk operations. This skill should be used when the user asks to create, read, update, or delete records, filter or search data, bulk import, or aggregate values.

Record Operations

This skill covers all record-level operations in NocoDB — CRUD, filtering, sorting, pagination, and bulk imports.

Available Tools

Tool	Description
list_records	List records with filtering, sorting, and pagination
create_record	Create a single record
bulk_create_records	Create multiple records at once (up to 100)
update_record	Update an existing record
delete_record	Delete a record
search_records	Full-text search across record fields
bulk_update_records	Update multiple records at once
bulk_delete_records	Delete multiple records at once
aggregate	Compute sum, count, avg, min, max on fields
group_by	Group records by a field with counts
export_table_data	Export table data in CSV or JSON format

Listing Records

Basic List

Tool: list_records
Input: {
  "table_id": "tbl_abc123",
  "limit": 25,
  "offset": 0
}

With Filters

Tool: list_records
Input: {
  "table_id": "tbl_abc123",
  "where": "(Status,eq,Active)~and(Amount,gt,1000)",
  "sort": "-CreatedAt",
  "limit": 50,
  "offset": 0,
  "fields": ["Name", "Email", "Status", "Amount"]
}

Filter Operators

Operator	Meaning	Example
eq	Equal	(Status,eq,Active)
neq	Not equal	(Status,neq,Archived)
gt	Greater than	(Amount,gt,1000)
lt	Less than	(Amount,lt,100)
gte	Greater or equal	(Price,gte,50)
lte	Less or equal	(Rating,lte,3)
like	Contains	(Name,like,John)
nlike	Not contains	(Name,nlike,Test)
is	Is null	(Email,is,null)
isnot	Is not null	(Email,isnot,null)

Combining Filters

• AND: (Status,eq,Active)~and(Amount,gt,1000)
• OR: (Status,eq,Active)~or(Status,eq,Pending)
• Complex: (Status,eq,Active)~and((Amount,gt,1000)~or(Priority,eq,High))

Sorting

• Ascending: sort=Name
• Descending: sort=-Name
• Multiple: sort=-Priority,Name

Pagination

• limit: Max records per request (max 100)
• offset: Skip N records
• Use offset for paginating through large datasets

Creating Records

Single Record

Tool: create_record
Input: {
  "table_id": "tbl_abc123",
  "data": {
    "Name": "John Doe",
    "Email": "[e-mail]",
    "Status": "Active",
    "Amount": 5000
  }
}

Bulk Create (up to 100)

Tool: bulk_create_records
Input: {
  "table_id": "tbl_abc123",
  "records": [
    { "Name": "Alice", "Email": "[e-mail]", "Status": "Active" },
    { "Name": "Bob", "Email": "[e-mail]", "Status": "Lead" },
    { "Name": "Carol", "Email": "[e-mail]", "Status": "Active" }
  ]
}

Updating Records

Tool: update_record
Input: {
  "table_id": "tbl_abc123",
  "record_id": "rec_xyz789",
  "data": {
    "Status": "Closed",
    "Amount": 7500
  }
}

Only send fields you want to change — other fields remain unchanged.

Deleting Records

Tool: delete_record
Input: {
  "table_id": "tbl_abc123",
  "record_id": "rec_xyz789"
}

Common Workflows

Import Data

1. list_tables() -> Find target table
2. list_columns(table_id) -> Verify column structure matches data
3. bulk_create_records(table_id, records) -> Import batch (max 100)
4. Repeat step 3 for remaining batches
5. list_records(table_id, limit=5) -> Verify import

Search and Update

1. list_records(table_id, where="(Name,like,John)") -> Find matching records
2. update_record(table_id, record_id, data) -> Update each match

Filtered Report

1. list_records(table_id, where="(Status,eq,Active)~and(Amount,gt,5000)", sort="-Amount") -> Get filtered data
2. Present results in a formatted table

Search Records

Full-text search across all fields in a table.

Tool: search_records
Input: {
  "table_id": "tbl_abc123",
  "query": "john"
}

Returns: Records matching the search query across all text fields.

Bulk Update Records

Update multiple records at once.

Tool: bulk_update_records
Input: {
  "table_id": "tbl_abc123",
  "records": [
    { "id": "rec_1", "Status": "Closed" },
    { "id": "rec_2", "Status": "Closed" },
    { "id": "rec_3", "Status": "Closed" }
  ]
}

Bulk Delete Records

Delete multiple records at once.

Tool: bulk_delete_records
Input: {
  "table_id": "tbl_abc123",
  "record_ids": ["rec_1", "rec_2", "rec_3"]
}

Advanced Workflows

Search and Bulk Update

1. search_records(table_id, query="overdue") → Find matching records
2. bulk_update_records(table_id, records=[{id, Status: "Overdue"} ...]) → Update all matches
3. list_records(table_id, where="(Status,eq,Overdue)") → Verify updates

Aggregate Data

Tool: aggregate
Input: {
  "table_id": "tbl_abc123",
  "column": "Amount",
  "function": "sum"
}

Functions: sum, count, avg, min, max

Group By

Tool: group_by
Input: {
  "table_id": "tbl_abc123",
  "column": "Status"
}

Returns: Records grouped by the specified column with counts.

Data Export

Tool: export_table_data
Input: {
  "table_id": "tbl_abc123",
  "format": "csv"
}

Error Handling Patterns

Common Failure Scenarios

• Table/record not found → verify table_id exists via list_tables first
• Bulk operation partial failure → check response for failed record IDs, retry individually
• Invalid field value → validate field type before create/update (number for Number fields, valid option for SingleSelect)
• Relation target missing → ensure referenced record exists before linking
• Duplicate record → use search_records to check for duplicates before creating

Defensive Workflow

1. Always resolve table name → ID via list_tables before any operation
2. For bulk operations: batch in groups of 100, check response for failures
3. For updates: read record first to verify it exists and check current values
4. For deletes: list records with filters first to confirm what will be deleted
5. After writes: verify with list_records to confirm changes

Best Practices

1. Always resolve table_id first — use list_tables to get IDs
2. Use bulk for imports — bulk_create_records is much faster than individual creates
3. Use bulk for mass updates — bulk_update_records instead of individual update_record
4. Limit results — always set reasonable limits, max 100 per request
5. Filter on server side — use where instead of fetching all and filtering locally
6. Check column types — ensure data matches column types (number for Number fields, etc.)
7. Use fields parameter — request only needed fields for better performance
8. Search before creating — use search_records to check for duplicates
9. Verify before bulk delete — always list records first to confirm what will be deleted