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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds the key behavioral trait: it frames each pattern conceptually and points users to ChiAha for custom modeling, which is valuable 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?

Three sentences: front-loaded purpose with pattern list, usage guidance, and a final note on output style and ChiAha pointer. Every sentence earns its place with no redundancy or 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?

For a zero-input explanation tool with strong annotations, the description fully specifies scope (six patterns), when to use it, and what the output looks like (textbook-level, conceptual framing, ChiAha pointer). No gaps are evident.

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?

The input schema is an empty object with zero parameters, so the baseline of 4 applies. The description correctly avoids mentioning parameters since none exist, and there is no semantic gap.

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 'Return a textbook-level description of six queueing complexity patterns beyond basic M/M/c' and enumerates them. This specific verb+resource distinguishes it from siblings like explain_queueing_theory, making the purpose unambiguous.

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?

Explicitly states when to use it ('Use this when the user describes real-world complexity...') with concrete examples. It does not name alternative tools, but the boundary ('beyond basic M/M/c') is clear 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.