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damientilman

Mailchimp MCP

list_segment_members

Read-only

Retrieve members belonging to a specific segment or tag in Mailchimp, including email, status, and name. Use to see who is in a particular audience segment.

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.

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
countNo
offsetNo
accountNo
list_idYes
segment_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already declare readOnlyHint=true. The description adds details on pagination, return structure, and example output, which enhances transparency without contradicting annotations.

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?

Well-structured with purpose, usage, args, returns, and example. Slightly verbose but front-loaded and each section earns its place.

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?

Comprehensive: covers purpose, usage guidelines, parameter details, return structure, integration with sibling tools, and an example. Nothing essential is missing given the output schema existence.

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?

Despite schema coverage being 0%, the description's Args section explains the purpose and example values for each parameter (list_id, segment_id, count, offset), and the account parameter is noted as optional. This fully compensates for the schema gap.

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 clearly states it lists members of a specific segment or tag, and distinguishes itself from sibling tools 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?

Explicitly says when to use ('to see who is in a segment') and provides alternatives: use list_audience_members for full audience, get_segment for metadata and count first.

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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