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AgentX-ai

Mailchimp MCP Server

by AgentX-ai

list_conversations

Retrieve all email conversations from your Mailchimp account to monitor customer interactions and track communication history.

Instructions

List all conversations

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Core handler implementing the tool logic: fetches conversations from Mailchimp API using paginated request to /conversations endpoint.
    async listConversations(): Promise<{
      conversations: MailchimpConversation[];
    }> {
      return await this.makePaginatedRequest(
        "/conversations",
        "timestamp",
        "DESC"
      );
    }
  • Tool registration in getToolDefinitions: defines name, description, and empty input schema for MCP tool listing.
    {
      name: "list_conversations",
      description: "List all conversations",
      inputSchema: {
        type: "object",
        properties: {},
        required: [],
      },
    },
  • Dispatcher handler in handleToolCall: calls service.listConversations() and returns formatted JSON summary of conversations.
    case "list_conversations":
      const conversations = await service.listConversations();
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(
              conversations.conversations.map((c) => ({
                id: c.id,
                subject: c.subject,
                from_email: c.from_email,
                timestamp: c.timestamp,
              })),
              null,
              2
            ),
          },
        ],
      };
  • Type definition for MailchimpConversation used in the output type of listConversations().
    export interface MailchimpConversation {
      id: string;
      message_id: string;
      list_id: string;
      from_email: string;
      from_label: string;
      subject: string;
      message: string;
      read: boolean;
      timestamp: string;
      _links?: Array<{
        rel: string;
        href: string;
        method: string;
        targetSchema?: string;
        schema?: string;
      }>;
    }
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'List all conversations' implies a read-only operation but doesn't specify if it requires authentication, how results are returned (e.g., pagination, sorting), or potential side effects. For a tool with zero annotation coverage, this leaves significant gaps in understanding its 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 extremely concise with 'List all conversations'—a single, clear sentence that front-loads the core action and resource. There is no wasted verbiage, making it efficient and easy to parse.

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 tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks context on behavior, output format, or usage compared to siblings. For a list operation, more detail on result handling would be beneficial, but the low complexity keeps it from being incomplete.

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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate. Baseline is 4 for 0 parameters, as the schema fully covers the absence of inputs.

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

Purpose3/5

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

The description 'List all conversations' clearly states the verb ('List') and resource ('conversations'), making the purpose understandable. However, it lacks specificity about scope or format, and while it distinguishes from sibling 'get_conversation' (singular vs. plural), it doesn't explicitly differentiate from other list_* tools like 'list_members' or 'list_campaigns' beyond the resource name.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for listing conversations, or compare it to sibling tools like 'get_conversation' for single conversations or other list_* tools for different resources. Usage is implied by 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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