mailchimp_list_automationsList the account's classic automations (automated email workflows). Paginated. REST: GET /automations.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | Records to return (Mailchimp `count`; default 10, max 1000). | |
| offset | No | Number of records to skip (Mailchimp `offset`, for pagination). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, and the description adds the paginated behavior and provides the REST endpoint, which is beyond what annotations convey. It also clarifies the scope ('account's classic automations'), giving useful context about what is retrieved. This exceeds the minimal bar for a safe read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, consisting of a single clear verb phrase plus a terse note on pagination and the REST endpoint. Every word contributes to understanding, and no filler or redundant information exists. The key action is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with good annotations and schema, the description is nearly complete. It states the resource type, scope, pagination behavior, and the REST reference. There's no output schema, but the return type (a list of automations) is implicit. It doesn't discuss edge cases like empty results or error handling, but that's not expected for such a straightforward list operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of the parameters (count and offset) with descriptive text, so the baseline is 3. The description adds no extra parameter meaning beyond noting 'Paginated,' which is already implied by the offset parameter. It does not elaborate on default values or constraints that the schema doesn't already provide.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists the account's classic automations (automated email workflows). The phrasing 'classic automations' with parenthetical clarification distinguishes it from potential sibling tools for campaigns or other resources. It leaves no ambiguity about the target resource.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: it operates at the account level and returns classic automations in a paginated manner. While it does not explicitly name alternatives or exclusions, the sibling tools serve different resource types (campaigns, audiences, etc.), making the intended use obvious from the resource name. The pagination hint also indicates it is the go-to tool for browsing automations.
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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Each tool targets a distinct resource-action pair, with clear distinctions between get/list/search for members and between add/update/archive for member changes. There is no meaningful overlap that would confuse an agent.
All tools follow the consistent mailchimp_verb_noun pattern in snake_case, making the action and resource predictable from the name. Deviation is minimal and stylistic.
With 18 tools, the count is slightly above the typical 3-15 range, but each tool covers a distinct Mailchimp resource (audiences, members, campaigns, reports, templates, automations, segments), so the breadth is justified.
The surface is heavily read-focused; write operations exist only for members (add, update, archive). Core workflows like creating or updating audiences, campaigns, templates, and segments are missing, which would hinder agents from performing typical Mailchimp tasks.