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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds meaningful behavioral context: it guarantees privacy ('This server never requests your incident notes and instructs your AI to keep them local') and documents response size behavior for each detail_level. No contradictions with 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 four sentences, each carrying distinct information: purpose/contents, rating-sheet reference, privacy guarantee, and parameter control. It is front-loaded with the core function and avoids filler.

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 tool has no output schema and four parameters, the description adequately covers return contents, response sizing, privacy behavior, and parameter interactions. It tells the agent what to expect (guidelines and rating-sheet items) and how to tailor the response, making it sufficient for tool selection and invocation.

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 descriptions already cover all four parameters (topics, detail_level, incident_type, include_examples), so baseline is 3. The description adds value by explaining that topics overrides detail_level for fine-grained control, that incident_type saves tokens, and by providing token estimates for detail_level options. This gives agents a deeper understanding of how to combine parameters effectively.

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 opens with a specific verb ('Load') and names the exact resource ('Lenny Zeltser's IR report writing context'), then enumerates what it returns (expert guidelines for field completeness, incident identification, notification triggers, and writing quality). This clearly distinguishes it from sibling load_context tools for other domains, though it doesn't explicitly differentiate from ir_get_guidelines.

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 clear usage context: 'for local analysis' and 'as concrete reference points for grounded feedback.' It also gives guidance on controlling response size via detail_level and explains the privacy guarantee. However, it does not explicitly state when to use this tool versus alternatives like ir_get_guidelines or other load_context tools.

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