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JurreBrandsenInfoSupport

Zendesk API MCP Server

list_macros

Retrieve and manage Zendesk macros for automating support responses, with pagination controls to handle large datasets.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number for pagination
per_pageNoNumber of macros per page (max 100)

Implementation Reference

  • The handler function that implements the core logic of the 'list_macros' tool: constructs params from input, calls zendeskClient.listMacros, and returns formatted JSON response or error.
    handler: async ({ page, per_page }) => {
      try {
        const params = { page, per_page };
        const result = await zendeskClient.listMacros(params);
        return {
          content: [{ 
            type: "text", 
            text: JSON.stringify(result, null, 2)
          }]
        };
      } catch (error) {
        return {
          content: [{ type: "text", text: `Error listing macros: ${error.message}` }],
          isError: true
        };
      }
  • Input schema using Zod for validating optional 'page' and 'per_page' parameters of the list_macros tool.
    schema: {
      page: z.number().optional().describe("Page number for pagination"),
      per_page: z.number().optional().describe("Number of macros per page (max 100)")
    },
  • src/server.js:48-52 (registration)
    Registration loop that calls server.tool() for each tool in allTools, which includes the 'list_macros' tool from macrosTools spread earlier in the array.
    allTools.forEach((tool) => {
      server.tool(tool.name, tool.schema, tool.handler, {
        description: tool.description,
      });
    });
  • Supporting method in ZendeskClient class that handles the API request to list Zendesk macros, invoked by the list_macros tool handler.
    async listMacros(params) {
      return this.request("GET", "/macros.json", null, params);
    }
Behavior1/5

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Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

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

Completeness1/5

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

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

Parameters1/5

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

Tool has no description.

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

Purpose1/5

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

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

Usage Guidelines1/5

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

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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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