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mq_send

Send messages between AI coding agents to coordinate tasks and delegate work across sessions and machines.

Instructions

Send a message to a target agent by name

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYes
messageYes
senderYes
msg_typeNotext
priorityNonormal
reply_toNo

Implementation Reference

  • The `send` function in `client.ts` implements the API call to the backend to send a message. This is invoked by the `mq_send` tool handler in `mcp/src/index.ts`.
    export async function send(
      target: string,
      message: string,
      sender: string,
      msgType = "text",
      priority = "normal",
      replyTo?: string,
    ) {
      const body: Record<string, string> = {
        target, message, from: sender, type: msgType, priority,
      };
      if (replyTo) body.reply_to = replyTo;
      return api("POST", "/send", body);
    }
  • mcp/src/index.ts:24-33 (registration)
    The `mq_send` tool is registered here, defining the input schema and the handler that calls `client.send`.
    server.tool("mq_send", "Send a message to a target agent by name", {
      target: z.string(),
      message: z.string(),
      sender: z.string(),
      msg_type: z.string().default("text"),
      priority: z.string().default("normal"),
      reply_to: z.string().optional(),
    }, async ({ target, message, sender, msg_type, priority, reply_to }) => ({
      content: [{ type: "text", text: JSON.stringify(await client.send(target, message, sender, msg_type, priority, reply_to)) }],
    }));
Behavior1/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure but fails to do so. It doesn't mention whether this is a read/write operation, authentication requirements, potential side effects (e.g., message delivery guarantees), rate limits, or error conditions, leaving critical behavioral traits unspecified.

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 that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy to grasp quickly.

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 complexity (6 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, parameter meanings, return values, and usage context, making it insufficient for an agent to effectively understand and invoke the tool.

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

Parameters2/5

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

Schema description coverage is 0%, so the schema provides no parameter details. The description adds minimal value by implying 'target' and 'message' parameters, but it doesn't explain the purpose or constraints of other parameters like 'sender', 'msg_type', 'priority', or 'reply_to', failing to compensate for the coverage gap.

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

Purpose4/5

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') and target ('to a target agent by name'), which is specific and actionable. However, it doesn't differentiate from sibling tools like mq_recv (receive) or mq_add (add to queue), leaving some ambiguity about its unique role.

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?

No guidance is provided on when to use this tool versus alternatives like mq_recv or mq_add. The description lacks context about prerequisites (e.g., needing to be logged in via mq_login) or scenarios where this tool is appropriate, offering minimal usage 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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