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get_list_readrequest

Poll the status of a large volume list read request and get notified when complete for page download.

Instructions

Poll status of a large volume list read. When status is COMPLETE, use get_list_readrequest_page to download each page. requestId comes from create_list_readrequest response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspaceIdYesAnaplan workspace ID or name
modelIdYesAnaplan model ID or name
listIdYesList ID or name
requestIdYesRead request ID (from create_list_readrequest response)

Implementation Reference

  • The MCP tool handler for 'get_list_readrequest'. Registers the tool with Zod schema for workspaceId, modelId, listId, and requestId, then calls the API and returns the JSON response.
    server.tool("get_list_readrequest", "Poll status of a large volume list read. When status is COMPLETE, use get_list_readrequest_page to download each page. requestId comes from create_list_readrequest response.", {
      workspaceId: z.string().describe("Anaplan workspace ID or name"),
      modelId: z.string().describe("Anaplan model ID or name"),
      listId: z.string().describe("List ID or name"),
      requestId: z.string().describe("Read request ID (from create_list_readrequest response)"),
    }, async ({ workspaceId, modelId, listId, requestId }) => {
      const wId = await resolver.resolveWorkspace(workspaceId);
      const mId = await resolver.resolveModel(wId, modelId);
      const lId = await resolver.resolveList(wId, mId, listId);
      const result = await apis.largeReads.getListReadRequest(wId, mId, lId, requestId);
      return { content: [{ type: "text" as const, text: JSON.stringify(result.listReadRequest ?? result, null, 2) }] };
    });
  • Input schema/validation for get_list_readrequest using Zod schema with four string parameters: workspaceId, modelId, listId, and requestId.
    workspaceId: z.string().describe("Anaplan workspace ID or name"),
    modelId: z.string().describe("Anaplan model ID or name"),
    listId: z.string().describe("List ID or name"),
    requestId: z.string().describe("Read request ID (from create_list_readrequest response)"),
  • The API helper method that executes the HTTP GET request to the Anaplan backend to fetch the list read request status.
    async getListReadRequest(
      workspaceId: string,
      modelId: string,
      listId: string,
      requestId: string
    ) {
      const res = await this.client.get<any>(
        `/workspaces/${workspaceId}/models/${modelId}/lists/${listId}/readRequests/${requestId}`
      );
      return res.listReadRequest ?? res;
    }
  • src/server.ts:59-61 (registration)
    Registration of bulk tools including get_list_readrequest via registerBulkTools, which wires the tool into the MCP server.
    registerBulkTools(server, {
      imports, exports, processes, files, client, modelManagement, calendar, versions, lists, largeReads, actions, optimizer,
    }, resolver);
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. It mentions polling and status transition but omits details like possible status values, error handling, rate limits, or any side effects. This is insufficient for a mutation-heavy context.

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?

Two sentences, no filler. Purpose is front-loaded, and the second sentence provides actionable usage guidance. Every word earns its place.

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?

The description explains the flow but lacks output schema details. For a polling tool, key information like response format, status values, and polling frequency are missing. Adequate but has clear gaps.

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% coverage with descriptions for all 4 parameters. The description reinforces the provenance of 'requestId' from 'create_list_readrequest', but adds little beyond the schema. Baseline 3 is appropriate.

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

Purpose5/5

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

The description clearly states the verb 'Poll' and the resource 'status of a large volume list read'. It also distinguishes from the sibling tool 'get_list_readrequest_page' by specifying when to use it.

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

Usage Guidelines4/5

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

The description provides clear context on when to use this tool (poll status) and when to use the sibling 'get_list_readrequest_page' (when status is COMPLETE). It also explains the origin of requestId. No explicit exclusions, but guidance is strong.

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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