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AgentX-ai

Mailchimp MCP Server

by AgentX-ai

get_conversation

Retrieve specific conversation details from Mailchimp to access email marketing interactions and customer communications.

Instructions

Get details of a specific conversation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
conversation_idYesThe conversation ID

Implementation Reference

  • The MCP tool handler for 'get_conversation'. It calls the MailchimpService.getConversation method with the provided conversation_id and formats the response as MCP tool content with JSON stringified data.
    case "get_conversation":
      const conversation = await service.getConversation(args.conversation_id);
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(conversation, null, 2),
          },
        ],
      };
  • Input schema definition for the 'get_conversation' tool, specifying an object with required 'conversation_id' string property.
      name: "get_conversation",
      description: "Get details of a specific conversation",
      inputSchema: {
        type: "object",
        properties: {
          conversation_id: {
            type: "string",
            description: "The conversation ID",
          },
        },
        required: ["conversation_id"],
      },
    },
  • src/index.ts:42-46 (registration)
    MCP server registration for listing tools, which includes the 'get_conversation' tool via getToolDefinitions.
    server.setRequestHandler(ListToolsRequestSchema, async () => {
      return {
        tools: getToolDefinitions(mailchimpService),
      };
    });
  • MailchimpService helper method that performs the API GET request to retrieve conversation details by ID.
    async getConversation(
      conversationId: string
    ): Promise<MailchimpConversation> {
      return await this.makeRequest(`/conversations/${conversationId}`);
    }
  • TypeScript interface defining the structure of a Mailchimp conversation (output type for the tool).
    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. It states the tool 'Get details' but doesn't clarify if this is a read-only operation, what permissions are required, how errors are handled, or the format of returned details. For a tool with no annotation coverage, this is a significant gap in transparency.

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 a single, efficient sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place by conveying the essential purpose.

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

Completeness2/5

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

Given the tool's complexity (a read operation with one parameter) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'details' include, potential return values, or behavioral aspects like error handling. This leaves the agent under-informed for effective use.

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

Parameters3/5

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

The input schema has 100% description coverage, with the parameter 'conversation_id' documented as 'The conversation ID'. The description adds no additional meaning beyond this, such as where to find the ID or format examples. Baseline 3 is appropriate since the schema does the heavy lifting.

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 'Get details of a specific conversation' clearly states the verb ('Get') and resource ('conversation'), but it's vague about what 'details' entails and doesn't distinguish this tool from siblings like 'list_conversations' or other 'get_' tools. It avoids tautology but lacks specificity.

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 like needing a conversation ID, contrast with 'list_conversations' for browsing, or specify use cases like retrieving metadata versus full content. This leaves the agent without contextual direction.

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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