generate-connector-spec

name	generate-connector-spec
description	Generate the connector spec YAML file defining connection parameters and external options allowlist.
disable-model-invocation	true

Generate Connector Spec YAML

Goal

Generate a connector spec YAML file for the {{source_name}} connector that defines the connector specification including connection parameters and external options allowlist.

Output Contract

Produce a YAML file following the template at templates/connector_spec_template.yaml as src/databricks/labs/community_connector/sources/{{source_name}}/connector_spec.yaml.

Requirements

Connection Parameters Structure

The template supports two structure options based on the connector's authentication complexity:

Option A: Single Authentication Method (Simple Case)

Use a flat parameters list directly under connection when the connector has only one way to authenticate:

connection:
  parameters:
    - name: api_key
      type: string
      required: true
      description: API key for authentication.

Option B: Multiple Authentication Methods

Use auth_methods when the connector supports multiple ways to authenticate (e.g., API key vs. OAuth, or service account vs. API secret). Shared parameters go in parameters at the connection level:

connection:
  auth_methods:
    - name: api_token
      description: Authenticate using email and API token.
      parameters:
        - name: email
          type: string
          required: true
          description: User email address.
        - name: api_token
          type: string
          required: true
          description: API token for authentication.

    - name: oauth
      description: Authenticate using OAuth 2.0.
      parameters:
        - name: access_token
          type: string
          required: true
          description: OAuth 2.0  

  
  
      
       
       
         

Determining Which Structure to Use

• Analyze the connector's __init__ method in src/databricks/labs/community_connector/sources/{{source_name}}/{{source_name}}.py.
• Look for conditional logic that checks for mutually exclusive credentials (e.g., "if username and secret ... elif api_secret ...").
• If the connector accepts multiple authentication approaches, use Option B with auth_methods.
• If there's only one way to authenticate, use Option A with flat parameters.

OAuth 2.0 Connections (the oauth block)

If the source authenticates via OAuth 2.0, add a connection.oauth block. This makes the connector use a Unity Catalog COMMUNITY connection in an OAuth auth mode instead of static credentials. The connection layer (UC, or the labs authenticate tool for local dev) runs the OAuth flow and injects an access token into the connector's options at query time — the user only supplies their OAuth app's client_id + client_secret, never a token.

This is where the OAuth flow is chosen. Selecting the right auth mode (m2m vs u2m vs u2m_per_user) is a research/build-time decision you make here from the source's supported grant types and the connector's intended access pattern. Once it is recorded in oauth.flow, the rest of the lifecycle (connection creation, deployment, end-user docs) just follows the spec — no one re-picks the mode downstream.

connection:
  parameters:
    - name: client_id
      type: string
      required: true
      secret: true
      description: OAuth 2.0 client ID for the provider app.
    - name: client_secret
      type: string
      required: true
      secret: true
      description: OAuth 2.0 client secret issued alongside the client_id.

  oauth:
    flow: u2m                  # m2m | u2m | u2m_per_user — maps to the connection auth mode
    pkce: true                 # set false for a confidential client that doesn't need PKCE
    scopes: "read"             # space-separated scopes requested at consent
    authorization_url: "https://provider/oauth/authorize"   # u2m / u2m_per_user
    token_url: "https://provider/oauth/token"
             
       
       

Choosing the flow

flow selects the auth mode and maps to the CLI's --auth-type / UC's community_oauth_flow. Pick from the grant types the source's API supports:

• m2m — the standard OAuth 2.0 client-credentials grant. No human and no browser: the connection posts client_id + client_secret (and scopes) to the token_url and gets back an app-level access token. Choose this when the source authenticates the application rather than an end user — service-to-service APIs, admin/tenant-wide tokens, anything with a "client credentials" or "server-to-server" grant. Only token_url (and optionally scopes) is needed; authorization_url / pkce / extra_auth_params do not apply.
• u2m — authorization-code flow where one human authorizes once at connection creation via a browser. Choose this when the source's data is scoped to a user account and a single shared identity (personal or team mailbox, one workspace login) is acceptable. Needs authorization_url + token_url.
• u2m_per_user — like u2m but each end user authorizes separately; UC resolves the right token per query at runtime. Choose this when different Databricks users querying the connection must each see their own data. Needs authorization_url + token_url.

Rule of thumb: no user identity involved → m2m; one user for the whole connection → u2m; each querying user is distinct → u2m_per_user. Determine the OAuth details (grant types, endpoints, scopes, whether PKCE is required) from the source API doc.

Other rules

• Do NOT list the OAuth-issued tokens (access_token / refresh_token) as required connection parameters — UC/the flow supplies them at runtime. List only the user-supplied app identity (client_id, client_secret) and any connector-specific options (e.g. user_id).
• pkce (default true) and extra_auth_params only affect the browser flows (u2m / u2m_per_user); set pkce: false for a confidential client that doesn't need it.
• The authorization_url / token_url / scopes are defaults baked into the spec so the user does not retype them; they can be overridden via --options at connection creation.
• The connector implementation should read the injected token (typically options["access_token"]) and treat it as opaque.
• Use this block in addition to Option A's flat parameters (it is not a substitute for the parameters list).

Parameter Documentation

For each parameter, document:

• name: The parameter name as used in the options dict
• type: The data type (string, integer, boolean, etc.)
• required: Whether the parameter is required (true/false within its context)
• description: A brief description of the parameter's purpose

For auth_methods, also include:

• name: A short identifier for the auth method (e.g., api_token, oauth, service_account)
• description: Explanation of when/why to use this method

Important: Do NOT include table-specific options in the connection parameters.

External Options Allowlist

• Review all read_table, get_table_schema, and read_table_metadata methods to identify table-specific options accessed from table_options.
• Compile all unique option names into a comma-separated string.
• Common table-specific options include:
  • Resource identifiers (e.g., owner, repo, account_id)
  • Filters (e.g., state, status, type)
  • Pagination controls (e.g., per_page, max_pages_per_batch)
  • Incremental sync options (e.g., start_date, lookback_seconds)
• If no table-specific options are used, set external_options_allowlist to an empty string.

Reference Sources

• Use the connector implementation (src/databricks/labs/community_connector/sources/{{source_name}}/{{source_name}}.py) as the primary source of truth.
• Cross-reference with the connector's README (src/databricks/labs/community_connector/sources/{{source_name}}/README.md) if available.
• If inconsistency found between the 2 above, please fix the README and flag errors to user.