Say something out loud in the meeting. It is spoken through the room's speakers and joins the transcript, so it is on the record like anything else that was said. Use sparingly — this interrupts people.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Keep it to a sentence or two. Nobody wants to be read a paragraph. | |
| meeting | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are present, the description must carry the full burden of transparency. It excels by revealing critical behavioral traits: the input method (spoken through speakers), the logging behavior (joins transcript/on the record), and the social cost (interruptions).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise. It is front-loaded, addressing the mechanism of speech and recording, and then concludes with a concise operational guideline.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low annotation coverage, the description is remarkably complete. It addresses the social impact, the delivery mechanism, and the length constraint, leaving no ambiguity for the AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 50% schema coverage, the description must add value. It does so by providing a crucial semantic constraint on the 'text' parameter: 'Keep it to a sentence or two. Nobody wants to be read a paragraph.' This is valuable social context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly defines the action ('Say something out loud') and the context ('in the meeting'). It distinguishes itself from message-based siblings like 'send_message' by specifying that the input is 'spoken through the room's speakers' and 'joins the transcript'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines by advising the agent to 'Use sparingly' and clearly stating the reason: 'this interrupts people.' This acts as a critical 'when-not-to-use' rule.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.