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Export subscribers with every column

export_subscribers
Read-onlyIdempotent

Export matched Substack subscribers as full records with engagement metrics. Creates an async export, waits until the file is ready, then downloads it.

Instructions

Export subscribers as full records, which is the only way to actually read the engagement metrics list_subscribers can only filter on: opens over 7d/30d/6mo, unique emails seen, post views, comments, shares, links clicked, days active and activity rating.

Substack builds the file asynchronously, so this creates a subscriber set, requests the export, polls until it is ready and downloads it. A small export takes a few seconds.

Two things verified against the live API and worth knowing:

  • tag_ids and group_membership cannot be exported. Substack drops them without failing, so they come back in missing_columns. Asking for all 48 returns 46.

  • Values arrive display-formatted, not raw. Revenue is "$50.00" here and the number 50 through list_subscribers. Dates are ISO strings.

There is no paging: an export covers the whole matching set.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchNoFree text matched against name and email.
columnsNoWhich columns to include. Defaults to all exportable ones: user_name, user_email_address, country, state, subscription_type, subscription_created_at, subscription_expires_at, first_payment_at, last_subscribed_at, unsubscribed_at, subscription_interval, stripe_plan_name, free_attribution, paid_attribution, is_subscribed, bestseller_tier, total_revenue_generated, num_subs_gifted, bundle_id, is_bundle_parent, num_emails_received, num_emails_dropped, num_email_opens, num_email_opens_last_7d, num_email_opens_last_30d, num_unique_email_posts_seen, num_unique_email_posts_seen_last_7d, num_unique_email_posts_seen_last_30d, last_opened_at, links_clicked, last_clicked_at, emails_enabled, num_web_post_views, num_web_post_views_last_7d, num_web_post_views_last_30d, num_unique_web_posts_seen, num_unique_web_posts_seen_last_7d, num_unique_web_posts_seen_last_30d, num_comments, num_comments_last_7d, num_comments_last_30d, num_shares, num_shares_last_7d, num_shares_last_30d, days_active_last_30d, activity_rating
filtersNoConditions, combined with AND. There is no OR and no nesting: anything needing OR has to be issued as separate calls. Operators by column type: - Int: is, is_not, gt, gte, lt, lte - String: is, is_not, is_any_of, contains, starts_with, ends_with, includes_none - DateTime: is_on, is_after, is_on_or_after, is_before, is_on_or_before - Array (tag_ids, emails_enabled): includes_any, includes_all, includes_none - subscription_type and group_membership: is, is_not, is_any_of Columns: user_name (String, "Name"); user_email_address (String, "Email"); country (String, "Country"); state (String, "State/Province"); group_membership (group_membership, "Group membership"); subscription_type (subscription_type, "Type"); subscription_created_at (DateTime, "Start date"); subscription_expires_at (DateTime, "Expiration date"); first_payment_at (DateTime, "First paid date"); last_subscribed_at (DateTime, "Paid upgrade date"); unsubscribed_at (DateTime, "Cancel date"); subscription_interval (String, "Subscription interval"); stripe_plan_name (String, "Stripe plan"); free_attribution (String, "Subscription source (free)"); paid_attribution (String, "Subscription source (paid)"); is_subscribed (Int, "Can see paid content"); bestseller_tier (Int, "Bestseller"); total_revenue_generated (Int, "Revenue"); num_subs_gifted (Int, "Subscriptions gifted"); bundle_id (Int, "Bundle"); is_bundle_parent (Int, "Bundle origin"); num_emails_received (Int, "Emails received (6mo)"); num_emails_dropped (Int, "Emails dropped (6mo)"); num_email_opens (Int, "Emails opened (6mo)"); num_email_opens_last_7d (Int, "Emails opened (7d)"); num_email_opens_last_30d (Int, "Emails opened (30d)"); num_unique_email_posts_seen (Int, "Unique emails seen (6mo)"); num_unique_email_posts_seen_last_7d (Int, "Unique emails seen (7d)"); num_unique_email_posts_seen_last_30d (Int, "Unique emails seen (30d)"); last_opened_at (DateTime, "Last email open"); links_clicked (Int, "Links clicked"); last_clicked_at (DateTime, "Last clicked at"); emails_enabled (Array, "Sections"); num_web_post_views (Int, "Post views"); num_web_post_views_last_7d (Int, "Post views (7d)"); num_web_post_views_last_30d (Int, "Post views (30d)"); num_unique_web_posts_seen (Int, "Unique posts seen"); num_unique_web_posts_seen_last_7d (Int, "Unique posts seen (7d)"); num_unique_web_posts_seen_last_30d (Int, "Unique posts seen (30d)"); num_comments (Int, "Comments"); num_comments_last_7d (Int, "Comments (7d)"); num_comments_last_30d (Int, "Comments (30d)"); num_shares (Int, "Shares"); num_shares_last_7d (Int, "Shares (7d)"); num_shares_last_30d (Int, "Shares (30d)"); days_active_last_30d (Int, "Days active (30d)"); activity_rating (Int, "Activity"); tag_ids (Array, "Tags")
publicationNoWhich connected publication to act on, matched loosely against its hostname (for example 'example.substack.com' or just 'example'). Defaults to the first connected publication.
max_wait_secondsNoHow long to wait for the file. 1 to 600, default 120.
Install Server

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds substantial behavioral detail beyond that: the asynchronous build/poll/download flow, the silent dropping of tag_ids and group_membership, display-formatted values versus raw values, and absence of paging. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every section earns its place: purpose, sibling differentiation, async behavior, verified API caveats, and paging semantics. The bullet-style caveats are scannable and the key facts are front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex export tool with no output schema, the description covers the workflow, caveats, and result scope well. The only minor gap is that it does not explicitly state the file format or exact return type, though 'downloads it' and display-format examples partially cover what the agent receives.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful parameter-related caveats, especially that tag_ids and group_membership cannot be exported and that defaults to all exportable columns. It also clarifies the whole matching set is exported with no paging, which informs the filters and columns parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Export subscribers as full records') and immediately differentiates it from list_subscribers by explaining that this is the only way to read engagement metrics. This makes the tool's purpose unambiguous and distinct from siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly contrasts with list_subscribers, saying list_subscribers can only filter on engagement metrics while export_subscribers returns actual full records. It also clarifies the async nature and the no-paging behavior, giving clear context for when this tool is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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