Using Claude AI to Build a Custom Connector With the Fivetran Connector SDK

Create a project directory named FDA_Tobacco_Claude. Inside this folder, add three key files to establish context for Claude:

• claude_md: A renamed copy of the agents.md file from the Fivetran Connector SDK public GitHub repo. It includes system goals, response formatting rules, and SDK-specific instructions.
• notes.txt: A manually prepared file containing authentication method, endpoint overview, sample API queries, and example JSON responses. This information is pulled directly from the FDA Tobacco API documentation.
• fields.yaml: A file downloaded from the API's documentation that describes the available fields in the dataset.

These files act as local prompt context Claude will reference when generating connector code.

In your terminal or command prompt, change into the FDA_Tobacco_Claude directory. Start a new Claude chat session and create a contextually-driven prompt that is divided into three sections:

Data source instructions

• Explain that the connector will extract data from the FDA Tobacco Problem Reports API
• Mention that the connector should support incremental sync and use checkpoints

Functional and development constraints

• Limit responses to the first 10 results per endpoint (to avoid unnecessary API load during testing)
• Add a requirement to exit gracefully
• Ask Claude to include clear comments and strategic logging
• Instruct Claude to define only the primary key and let Fivetran infer the rest

Context references and execution location

• Point to claude_md, notes.txt, and fields.yaml in the current folder
• State that the solution should be written to this project directory and follow Connector SDK best practices

Ask Claude to generate the following files:

• connector.py: The main connector script that handles authentication, schema definition, and sync logic.
• configuration.json: Configuration template with required parameters.
• requirements.txt: Dependency list for Python packages.

Claude proceeds to generate each file in sequence:

• It first creates connector.py and outlines its structure.
• Then, it generates a basic requirements.txt containing packages such as fivetran_sdk.
• Finally, it writes out configuration.json using values referenced in the notes.

Once completed, confirm that all files now exist in your project directory.

Prompt Claude to test its own output by running:

fivetran debug connector.py --configuration configuration.json

The debug should succeed with the following summary:

• 10 upserts performed
• 1 checkpoint created
• 1 schema change detected

Ask Claude to inspect the output database (warehouse.db) that was created by the Connector SDK during the debug run. Claude reads and presents a sample of the replicated records.

This confirms that data was fetched, processed, and written according to the connector logic.

Ask Claude to deploy the connector to Fivetran using:

fivetran deploy

Claude prompts for additional details required by the CLI:

• Destination: csg_auto_test
• Connection name: FDA tobacco video
• Configuration file: configuration.json

Once these values are provided, Claude submits the deployment command. The CLI confirms that the deployment was successful and provides a direct link to the new connector in the Fivetran UI.

Use the deployment link to open the new connector in your Fivetran dashboard. From there, start the initial sync.

Claude confirms that:

• A schema and associated tables are created in Snowflake
• Replicated data has successfully loaded from the FDA API

You began with a project folder containing just notes.txt and fields.yaml, then added a Claude-readable claude_md derived from agents.md. With those context files in place, you:

• Wrote a multi-part structured prompt
• Instructed Claude to generate all required Connector SDK files
• Tested the connector using fivetran debug
• Verified the output with Claude
• Deployed and synced the connector

The entire process was completed without writing any code manually — all through Claude.