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

With no annotations, the description carries full burden and succeeds: it discloses voice/text mode behavior, the mic-opening side effect of expect_reply, and formatting constraints (no lists, markdown, or long sentences). These are important behavioral traits beyond what the schema conveys.

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 appropriately brief, front-loaded with the core purpose, and each subsequent sentence adds essential usage or behavioral detail. No filler or redundant restatement of the schema.

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?

For a simple two-parameter tool with an output schema, the description covers purpose, usage modes, formatting rules, parameter behavior, and the natural follow-up action. It is fully sufficient for an agent to select and invoke correctly.

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?

Schema coverage is 100%, so baseline is 3. The description adds semantic value by explaining the expect_reply default and its behavioral consequence ('the microphone opens as soon as you stop talking'), and reinforces the text formatting constraints. This goes beyond the schema's parameter descriptions.

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 states the tool's purpose: 'Speak in your own voice — greetings, probes, transitions, feedback.' It uses a specific verb ('speak') and resource ('your own voice'), and distinguishes it from sibling tools like listen, probe, and ask_case_prompt by framing it as the agent's spoken chat line.

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?

It provides strong contextual guidance: keep it conversational/brief, no lists/markdown, and explains that expect_reply opens the mic. It explicitly names 'listen' as the follow-up step, but does not contrast when to use this versus other speaking tools like probe or ask_case_prompt, so it lacks explicit exclusions or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.