> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nekt.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Meetime as a data source
> Bring data from Meetime to Nekt.
Meetime is a sales engagement and meeting scheduling platform that helps sales teams connect with prospects and customers. It provides tools for meeting scheduling, follow-up automation, and sales pipeline management to improve conversion rates and customer relationships.
## Configuring Meetime as a Source
In the [Sources](https://app.nekt.ai/sources) tab, click on the "Add source" button located on the top right of your screen. Then, select the Meetime 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 Meetime data.
The following configurations are available:
* **API Token**: Your Meetime authorization token. Check [Meetime's documentation](https://meetime.readme.io/reference/autentica%C3%A7%C3%A3o) to discover where you can find this parameter.
Once you're done, click **Next**.
### 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.
Once you are done configuring, click **Next**.
### 4. Configure data source
Describe your data source for easy identification within your organization, not exceeding 140 characters.
To define your [Trigger](https://docs.nekt.com/runs/scheduling-and-triggers), 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](https://docs.nekt.com/get-started/core-concepts/resource-control).
* Determine when to execute an **Additional [Full Sync](https://docs.nekt.com/get-started/core-concepts/types-of-sync#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.
Once you are ready, click **Next** to finalize the setup.
### 5. Check your new source
You can view your new source on the [Sources](https://app.nekt.ai/sources) page. If needed, manually trigger the source extraction by clicking on the arrow button. Once executed, your data will appear in your Catalog.
For you to be able to see it on your [Catalog](https://app.nekt.ai/catalog), you need at least one successful source run.
# Streams and Fields
## Available streams
The table below lists every stream, its **slug** (the exact identifier to pass when creating the source via API) and a short description.
| Stream | Slug | Description |
| ------------------------- | --------------------------- | ------------------------------------------------------------------------- |
| Cadences | `cadences` | Prospecting cadences (sequences of outreach steps) configured in Meetime. |
| Calls | `calls` | Sales calls logged in Meetime, including outcome and metadata. |
| Demos | `demos` | Product demos and meetings scheduled with leads. |
| Leads | `leads` | Leads (prospects) tracked in Meetime. |
| Prospections | `prospections` | Prospection records tying leads to cadences during outreach. |
| Prospections Activities | `prospections_activities` | Individual activities executed within each prospection. |
| Prospections Lost Reasons | `prospections_lost_reasons` | Reasons registered when a prospection is marked as lost. |
| Users | `users` | Meetime users (sales reps and team members) in the account. |
## Fields by stream
Below you'll find all available data streams from Meetime:
Stream for retrieving cadence data from Meetime.
Stream for retrieving call data from Meetime.
Stream for retrieving demo data from Meetime.
Stream for retrieving lead data from Meetime, including custom fields.
Stream for retrieving prospection data from Meetime.
Stream for retrieving prospection activities data from Meetime.
Stream for retrieving prospection lost reasons from Meetime.
Stream for retrieving user data from Meetime.
## Skills for agents
Meetime connector documentation as plain markdown, for use in AI agent contexts.