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Send message as operator

dosai_send_operator_message

Send a message to a real client in WhatsApp, Instagram or Telegram, as a human operator. Works for a WhatsApp group dialog too (kind=group): the message goes into the group from the owner's number and every member sees it. This is visible to real people and cannot be unsent, so confirm the wording with the user before calling. No dialog for that phone yet? Open one with open_conversation first.

Input Schema

NameRequiredDescriptionDefault
textYesExactly what the client will see.
project_idYesProject id, as returned by dosai_list_projects.
conversation_idYesConversation id, as returned by dosai_list_conversations.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Adds behavior the annotations cannot express: the message is visible to real people, cannot be unsent, and in group mode is broadcast to every group member from the owner's number. This is exactly the side-effect context an agent needs before firing a non-idempotent, open-world write.

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

Conciseness4/5

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

Four short sentences, front-loaded with the core action and escalation to the safety warning and prerequisite. Efficient overall, though the 'kind=group' phrasing refers to a non-existent parameter and distracts rather than informs.

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?

For a 3-param write with no output schema, the description covers audience, irreversibility, group semantics and the conversation-creation prerequisite. It leaves out failure behavior and rate limits, which are minor gaps rather than blockers.

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%, so the schema already documents project_id, conversation_id and text, giving a baseline of 3. The description's only parameter-level claim is 'kind=group', a field that does not exist in the input schema, so it adds no usable meaning and slightly muddies the parameter model.

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?

States a specific verb and resource ('send a message ... as a human operator') plus the channels it covers (WhatsApp, Instagram, Telegram) and the group variant. An agent can distinguish it from schedule_message, send_report_now and toggle_bot_in_conversation without inspecting schemas.

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?

Gives a concrete prerequisite path ('No dialog for that phone yet? Open one with open_conversation first') and a procedural guard ('confirm the wording with the user before calling'). It does not contrast with the scheduling sibling, so the when-to-use-vs-alternative picture is not fully closed.

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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