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Export subscribers with every column

export_subscribers
Read-onlyIdempotent

Download all matching Substack subscribers as full records to access engagement metrics—opens, views, comments, shares, clicks, and activity—that filtering alone can't return.

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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv3.0.1
    • removedInput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
    • removedInput schema / additionalProperties
      Removed value: -false
    • removedInput schema / properties / filters / items / additionalProperties
      Removed value: -false
  2. First observedv2.1.1

TDQS

A4.5/5.0
Behavior5/5

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

Adds substantial behavior beyond the annotations: the asynchronous lifecycle (create set, request, poll, download), a latency expectation, two verified API quirks (tag_ids/group_membership silently dropped and returned in missing_columns, 48 requested yields 46), display-formatted vs raw values, and the absence of paging.

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

Conciseness4/5

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

Front-loaded with purpose and the list_subscribers contrast, then grouped into behavior, verified quirks, and paging. Dense and useful, though the long enumeration of metrics in the opening sentence is slightly heavy.

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?

With no output schema, the description carries the return-value burden and does so well for an async tool: it explains polling, download, value formatting, and column drops. It stops short of describing the delivered file format or naming the output artifact, which is a minor remaining gap.

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 schema already documents the parameters; the description still adds meaning by clarifying that columns defaults to all exportable ones and that two requested columns can never be returned. It reinforces the AND-only/no-OR filter model, though that is already in the schema.

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?

States a specific verb and resource (export subscribers as full records) and contrasts directly with the sibling list_subscribers, explaining that this is the only way to read engagement metrics the sibling can merely filter on. An agent can distinguish it from list_subscribers without opening either schema.

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

Usage Guidelines4/5

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

Clearly routes the agent: use list_subscribers to filter on engagement metrics, use this tool to actually read them. No explicit 'do not use when' guidance, but the alternative and its selection condition are named.

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