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Send Task Message

send_task_message

Send a message to the human worker on your task to answer clarifying questions, add context, or follow up. Messages are scoped to a single task and visible to the assigned worker.

Instructions

Send a message to the human worker on one of your tasks. Use this to answer a clarifying question, add context, or follow up. Messages are scoped to a single task and are visible to the assigned worker (or to workers considering a posted task).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
task_idYesThe task ID returned from post_task
bodyYesMessage body, 1-2000 characters

Implementation Reference

  • Registration and handler for the send_task_message tool. Calls either the API client (real mode) or mock function (mock mode) to send a message to a human worker on a task.
    export function registerSendTaskMessage(
      server: McpServer,
      client: ApiClient | null,
    ): void {
      server.registerTool(
        'send_task_message',
        {
          title: 'Send Task Message',
          description:
            'Send a message to the human worker on one of your tasks. Use this to answer a clarifying question, add context, or follow up. Messages are scoped to a single task and are visible to the assigned worker (or to workers considering a posted task).',
          inputSchema: z.object({
            task_id: z
              .string()
              .uuid()
              .describe('The task ID returned from post_task'),
            body: z
              .string()
              .min(1)
              .max(2000)
              .describe('Message body, 1-2000 characters'),
          }),
          annotations: { readOnlyHint: false, destructiveHint: false },
        },
        async (args) => {
          try {
            const result = client
              ? await client.sendTaskMessage({ task_id: args.task_id, body: args.body })
              : mockSendTaskMessage({ task_id: args.task_id, body: args.body });
    
            return {
              content: [
                {
                  type: 'text' as const,
                  text: `Message sent on task ${result.message.task_id} (id: ${result.message.id}).`,
                },
              ],
              structuredContent: result as unknown as Record<string, unknown>,
            };
          } catch (error) {
            return toolError(error);
          }
        },
      );
    }
  • Input interface for send_task_message: requires task_id (UUID string) and body (1-2000 chars).
    export interface SendTaskMessageInput {
      task_id: string;
      body: string;
    }
  • Response interface for send_task_message: returns a TaskMessageRow object.
    export interface SendTaskMessageResponse {
      message: TaskMessageRow;
    }
  • src/server.ts:15-16 (registration)
    Import of registerSendTaskMessage in the server setup file.
    import { registerSendTaskMessage } from './tools/send-task-message.js';
    import { registerListTaskMessages } from './tools/list-task-messages.js';
  • src/server.ts:61-62 (registration)
    Registration call: wires send_task_message into the MCP server during createServer().
    registerSendTaskMessage(server, client);
    registerListTaskMessages(server, client);
Behavior4/5

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

Annotations already indicate non-read-only (readOnlyHint=false) and non-destructive (destructiveHint=false). The description adds context about message scope and visibility to workers, which is helpful beyond annotations.

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?

Three concise sentences with no wasted words, front-loaded with the action and purpose.

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

Completeness4/5

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

The tool is simple with no output schema, but the description adequately explains behavior and scope. Missing return value info is minor given the context.

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?

Schema coverage is 100% with good descriptions for both parameters. The description does not add significant new meaning beyond the schema, earning the baseline score.

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 action ('Send a message to the human worker') and the resource ('one of your tasks'). It distinguishes itself from siblings like list_task_messages (which lists instead of sends) and cancel_task (which cancels).

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?

It provides explicit use cases: 'answer a clarifying question, add context, or follow up.' However, it does not explicitly mention when not to use or alternatives, so not a perfect score.

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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