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Mailchimp MCP Server

Mailchimp MCP Server

by AlexlaGuardia
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

list_segments

Retrieve saved segments for a Mailchimp audience using its list ID, with optional count and offset parameters to control results.

Instructions

List saved segments for an audience.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "list_id": "<list_id>"
    },
    "name": "list_segments"
  }
}

Input Schema

NameRequiredDescriptionDefault
list_idYes
countNo
offsetNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.8/5.0
Behavior2/5

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

No annotations exist, and the description does not disclose behavioral traits such as read-only, authentication needs, or side effects. The verb 'List' implies a read operation, but this is not explicit, and no further context is given.

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?

The description is a single sentence of five words, making it very concise. However, it may be too brief to convey sufficient information for an AI agent. It wastes no words but could include more substance without becoming verbose.

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

Completeness3/5

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

Given the presence of an output schema and the simplicity of the operation (listing segments), the description is minimally adequate. However, without annotations or detailed parameter info, it lacks completeness for an autonomous agent, especially among many sibling tools.

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

Parameters2/5

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

Schema description coverage is 0%, and the description adds no meaning beyond parameter names. While names like count and offset are standard, the tool provides no elaboration on their roles or constraints, such as acceptable values or behavior.

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

Purpose4/5

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

The description clearly states the action (list) and the resource (saved segments for an audience). It is specific and distinct from sibling tools like list_audiences or list_members, though it does not explicitly differentiate itself.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of context, prerequisites, or exclusions, leaving the agent to infer usage from the name alone.

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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