Configuring MeetRox as a Source
In the Sources tab, click on the “Add source” button located on the top right of your screen. Then, select the MeetRox option from the list of connectors. Click Next and you’ll be prompted to add your access.1. Add account access
You’ll need to authorize Nekt to access your MeetRox data. The following configurations are available:- API Key: Your MeetRox API key for authentication. Contact MeetRox support or check your account settings to obtain this key.
- Initial sync date (optional, advanced): Starting point for data extraction. Only records created or updated after this date will be synced. If not set, all available data will be extracted.
- Fetch Call Details (optional, advanced): When enabled, the connector makes one additional API request per call to fetch the full call details (analysis, scorecard, evaluators and screen-share percentage). This increases sync time and API usage, so keep it off unless you need those fields.
2. Select streams
Choose which data streams you want to sync. For faster extractions, select only the streams that are relevant to your analysis. You can select entire groups of streams or pick specific ones.Tip: The stream can be found more easily by typing its name.Select the streams and click Next.
3. Configure data streams
Customize how you want your data to appear in your catalog. Select the desired layer where the data will be placed, a folder to organize it inside the layer, a name for each table (which will effectively contain the fetched data) and the type of sync.- Layer: choose between the existing layers on your catalog. This is where you will find your new extracted tables as the extraction runs successfully.
- Folder: a folder can be created inside the selected layer to group all tables being created from this new data source.
- Table name: we suggest a name, but feel free to customize it. You have the option to add a prefix to all tables at once and make this process faster!
- Sync Type: you can choose between INCREMENTAL and FULL_TABLE.
- Incremental: every time the extraction happens, we’ll get only the new data, which is good if, for example, you want to keep every record ever fetched.
- Full table: every time the extraction happens, we’ll get the current state of the data, which is good if, for example, you don’t want to have deleted data in your catalog.
4. Configure data source
Describe your data source for easy identification within your organization, not exceeding 140 characters. To define your Trigger, consider how often you want data to be extracted from this source. This decision usually depends on how frequently you need the new table data updated (every day, once a week, or only at specific times). Optionally, you can define some additional settings:- Configure Delta Log Retention and determine for how long we should store old states of this table as it gets updated. Read more about this resource here.
- Determine when to execute an Additional Full Sync. This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while.
5. Check your new source
You can view your new source on the Sources page. If needed, manually trigger the source extraction by clicking on the arrow button. Once executed, your data will appear in your Catalog.Streams and Fields
Below you’ll find all available data streams from MeetRox:Calls
Calls
Stream for retrieving call records from MeetRox, including interaction metrics, business information, CRM associations and attendee details. Records are synced incrementally by
updated_at. Nested objects are delivered as structs; their inner fields are listed below with dot notation.Fields:Implementation Notes
Incremental Sync
Calls are requested ordered byupdated_at ascending and filtered by updated_at >= the saved bookmark (or the Initial sync date on the first run), so each run only fetches calls created or edited since the previous one. Pagination follows the API’s cursor (meta.end_cursor).
Data Type Coercion
The MeetRox API is loose about scalar types: decimals such asduration or interaction_metrics.ai_score may arrive as strings (e.g. "1696.14"), and text fields such as default_answer may arrive as numbers or booleans. Because the loader builds each column from the declared type, a single mismatched value would fail the whole batch. The connector therefore reconciles scalars with the declared schema type in both directions during extraction:
- Text that spells a number is parsed into the declared Integer or Number type.
- Numbers and booleans found where the schema declares a String are cast to text.
- A string that cannot be parsed as a number (e.g.
"n/a") is kept as text and a warning is logged, rather than dropping the record.
CRM Associations
The API returnsassociations as an object grouped by entity type (crm_deals, crm_contacts, …). The connector flattens these groups into a single array of link objects, keeping the group name in entity_type, so the field always matches the declared array schema.
Scorecard Timestamps
Timestamps inside thescorecard struct (inserted_at / updated_at of the scorecard, its categories and questions) are intentionally delivered as ISO-8601 text rather than DateTime. Nested date-times are not converted by the loader and would break the batch, and these values are only metadata of the scorecard template. The API also misspells the scorecard’s updateted_at, which the connector maps to updated_at.
Call Details and Rate Limiting
The MeetRox API allows 100 requests per minute. When Fetch Call Details is enabled, detail requests are spaced roughly 0.7 seconds apart, and429 responses honour the X-RateLimit-Reset header before retrying. A detail request that still fails after 5 attempts is skipped (the call record is kept without its detail fields) and a warning is logged; after 6 skipped calls, further warnings are suppressed to keep the log readable.
Skills for agents
Download MeetRox skills file
MeetRox connector documentation as plain markdown, for use in AI agent contexts.