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damientilman

Mailchimp MCP

list_segment_members

Retrieve all email subscribers belonging to a specific audience segment, including their status and merge fields.

Instructions

List individual members belonging to a specific segment or tag.

Use to see who is in a segment. Use list_audience_members to browse all members of the full audience instead. Use get_segment to check segment metadata and member count first.

Authenticated via API key. Subject to Mailchimp API rate limits (max 10 concurrent requests). Read-only, safe to retry.

Args: list_id: The Mailchimp audience/list ID (e.g. 'abc123def4'). Obtain from list_audiences. segment_id: The segment ID (numeric string, e.g. '12345'). Obtain from list_segments. count: Number of members to return (1-1000, default 20). offset: Pagination offset. Use when total_items exceeds count.

Returns: JSON with total_items and members array. Each member: id, email_address, status, full_name, merge_fields (object with FNAME, LNAME, etc.).

Example: list_segment_members(list_id="abc123", segment_id="12345", count=50) -> {"total_items": 150, "members": [{"email_address": "jane@co.com", ...}]}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
list_idYes
segment_idYes
countNo
offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Without annotations, the description fully discloses authentication ('Authenticated via API key'), rate limits ('max 10 concurrent requests'), idempotency ('Read-only, safe to retry'), and pagination behavior.

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 well-structured with a clear purpose, usage guidance, parameter details (Args/Returns), and an example. It is front-loaded and every sentence adds value without redundancy.

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

Completeness5/5

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

Given the tool's complexity (4 parameters, output schema), the description covers purpose, usage, parameters, return format, and pagination. It provides a complete picture for an agent to select and invoke correctly.

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

Parameters5/5

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

The description adds essential meaning beyond the schema: examples for list_id and segment_id, constraints (numeric string, range 1-1000 for count), and data source instructions, fully compensating for the 0% schema coverage.

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 starts with 'List individual members belonging to a specific segment or tag,' clearly stating the verb 'list' and the resource. It distinguishes from siblings by referencing list_audience_members and get_segment.

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?

The description explicitly tells when to use this tool ('Use to see who is in a segment') and directs to alternatives for other tasks, providing clear context and exclusions.

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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