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

Annotations only indicate readOnly=false, destructive=false, idempotent=false. The description adds meaningful behavior: it requires at least 3 usable text messages and it 'Runs an LLM call,' which signals cost/latency. It does not contradict the annotations.

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?

Four sentences with no filler. The main purpose and source are front-loaded, followed by author semantics, group-chat handling, a threshold constraint, and the LLM-call side effect. Every sentence carries operational value.

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?

Invocation guidance is strong: input source, author choices, group disambiguation, minimum message count, and LLM cost are all covered. However, with no output schema, the description does not state what is returned (e.g., a persona ID/artifact) or how the result feeds into downstream tools like neuron_apply_persona_to_bot, which is a meaningful gap.

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 high (83%), and the description adds real meaning beyond properties: it explains what 'author' values represent ('me' means owner's outgoing texts, 'contact' means the other party) and how senderName/senderPhone isolate a group participant. The schema already documents conversationId's source, so the agent has enough combined guidance.

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 states a specific action and resource: 'Distill a reusable voice from an existing WhatsApp conversation's stored messages.' It also clarifies 'no extension needed' and names the exact source material, making it easy to distinguish from siblings like neuron_build_persona_from_text and neuron_build_persona.

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 gives clear context for when to use it: with a stored WhatsApp conversation, choosing 'author' for voice, and using senderName/senderPhone for group chats. It does not explicitly name alternatives or state when not to use it, but the context is strong enough to guide selection.

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