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

Annotations show readOnlyHint=false and destructiveHint=false, consistent with a write operation. The description adds that the note builds durable context for follow-ups and is stored separately from raw history, providing useful behavioral context beyond 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?

The description is two sentences plus a parenthetical, front-loaded with the main action. Every sentence adds value: purpose, usage instructions, storage detail. No wasted words.

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 write tool without an output schema, the description adequately covers purpose and parameter guidance. It does not explain return values or post-logging behavior, but that is acceptable for such a tool.

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 value by guiding the agent to derive pain points and sentiment from the conversation, thereby enriching the meaning of those parameters.

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 saves a structured summary into the CRM for a lead. It distinguishes from siblings like save_lead_message by noting it is stored separately from raw LinkedIn message history, and instructs the agent to analyze context before calling.

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 explicitly tells the agent to analyze the chat/context first, then call the tool with a summary, pain points, and sentiment. It implies when to use (after analysis) versus raw message logging, but does not provide explicit when-not or list alternative tools.

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