Use this file to discover all available pages before exploring further.
This guide walks through the order in which to create resources for a complete Prequel Import pipeline. Each step links to the corresponding API reference page and includes an expandable curl example.
Get started with the Prequel agent skill, the Prequel MCP server, a Postman collection, or curl commands.
Agent skill
MCP server
Postman
curl commands
Skills are an open standard for packaging reusable agent capabilities. Claude Code, Cursor, and other skill-aware agents can all load the Prequel Import skill.
1
Save the skill to your agent's skills directory
Open the raw skill file, copy the contents, and save the file to your agent’s skills directory. For Claude Code use ~/.claude/skills/prequel-import-quickstart/SKILL.md. For Cursor use .cursor/skills/prequel-import-quickstart/SKILL.md in your project root and reload the workspace.
2
Prompt your agent
Begin an agentic Prequel Import configuration using this prompt.
Mintlify auto-hosts an MCP server for this documentation site, exposing search and filesystem tools your agent can call to look up the right operations and schemas.
1
Connect your AI tool to the Prequel Import MCP server
Add https://import-docs.prequel.co/mcp to your tool’s MCP configuration. Most clients accept the URL directly through their MCP settings.
2
Prompt your agent
Begin an agentic Prequel Import configuration using this prompt.
1
Import the OpenAPI spec into Postman
In Postman, click Import, paste the Prequel Import OpenAPI spec URL, and confirm. Postman generates a complete collection from the spec with one request per operation.
2
Set your API key as a collection variable
Add a PREQUEL_API_KEY variable to the imported collection and reference it from each request’s X-API-Key header.
1
Open the API reference
Browse the API reference and select the operation for the resource you want to create.
2
Copy the sample request body to your clipboard
Use the Copy button on each operation’s example request body, paste it into your shell as the -d payload, and substitute your variables and resource IDs as you go.
You will need a Prequel API key. If you do not have one, contact your Prequel representative to get access. The API key used in the X-API-Key header will route your request to either production or staging based on the key.
Prequel Import should follow a three phase setup to prevent errors from missing dependencies:
Prepare your environment by creating a Datalake. The Datalake is a one-time setup shared across all Providers.
Configure and validate a data source by creating a Provider, Source, and Dataset, then triggering an Extract to confirm the Source, Dataset, and Datalake are configured correctly before adding delivery.
Connect and deliver by standing up a delivery endpoint, creating a Destination (and optionally a custom Product), creating a Stream, testing the transform, and triggering a Load.
Each call returns a resource ID in the response body. Record the IDs as you go, since later steps reference them with placeholders such as <DATALAKE_ID> and <DESTINATION_ID>.
Provision a bucket on S3, GCS, Azure Blob Storage, or an S3-compatible store to back the Datalake, then create the Datalake referencing that bucket. Typically, a single multi-tenant Datalake is created and reused across Providers, since Prequel automatically partitions the bucket internally by Provider. You may create and assign additional Datalakes if you prefer bucket-level isolation for Providers. See Datalake for configuration steps and open the API reference for the full request schema.
Repeat this phase for each Provider you onboard. Each Provider can have multiple Sources and multiple Datasets.
1
Create a Provider
A Provider represents a customer or tenant of your platform. The Provider references the Datalake by ID and the assigned Products. Pass the special ["all"] Product to grant access to every Destination when customers share all Destinations. If a customer should only have access to a subset of Destinations, use Products to restrict Destinations by Provider. See Providers, or open the API reference for the full request schema.
A Source is the Provider’s database Prequel will read from. See Sources for the supported vendors and per-vendor fields and open the API reference for the full request schema.
A Dataset represents a structured table of data that Prequel will replicate from the Source. The Dataset declares its extraction method (table for a single table, sql for a custom query, glob for object-storage paths) and certain special columns and configuration options. This step is repeated for each Provider’s table to be imported. See Datasets and Change Detection for incremental sync configuration, or open the API reference for the full request schema.
The Extract pulls Dataset rows into the Datalake. Trigger one now to confirm the Source, Dataset, and Datalake are configured correctly before moving on to delivery. A successful Extract verifies authentication to the Source, read access to the Dataset’s table, and write access to the Datalake. Open the API reference for the full request schema.
Show example: POST /datasets/{id}/extract
curl -X POST https://api.prequel.co/import/datasets/<DATASET_ID>/extract \ -H "X-API-Key: $PREQUEL_API_KEY"
A Destination is one of your delivery endpoints. Each Destination is configured with a record_schema that describes the records your endpoint expects and a request_template that defines the URL and auth headers. See Destinations and the Batch API specification, or open the API reference for the full request schema.Repeat this step for each Destination you need.
Confirm your delivery endpoint from the prerequisites is reachable. Prequel validates connectivity during creation.
By default, every Destination you create is included in a built-in all Product. Skip this step and reference all when you create a Provider, unless you want to group Destinations differently. To define a custom group, create a Product and include the Destination IDs you want it to cover. Open the API reference for the full request schema.
A Stream maps one Dataset to one Destination through field-level mappings. Before authoring the mappings, fetch the Destination’s record_schema so you know which fields your mappings need to cover. See Streams and Advanced Table Mapping, or open the API reference for the full request schema.
Apply the Stream’s mappings to a sample of extracted records and confirm the output covers every required field in the Destination’s record_schema with the correct type. Open the API reference for the full request schema.
The Load applies the Stream’s mappings and delivers records to the Destination. Open the API reference for the full request schema. Then, verify records have reached your delivery endpoint.