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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds 'Pure static text — no engine call, deterministic output,' which confirms no side effects and clarifies the tool's behavior 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 four sentences, front-loaded with the core purpose. Every sentence adds value: purpose, topics covered, usage note, and behavioral 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?

Given no parameters and an output schema exists, the description fully covers what the tool does, what it returns, and when to use it. It mentions the content scope and the underlying engine (AMOS optimizer) for context. No gaps.

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 tool has no parameters (schema empty, coverage 100%). As per rule, baseline is 4. The description does not need to explain parameters since there are none, and it doesn't waste space on unnecessary details.

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 it provides 'Reference text on supply-chain network optimization' and lists specific topics (MIP, decision variables, constraints, etc.). It also explicitly says when to use: 'when the user asks a conceptual how does network optimization work question.' This distinguishes it from sibling tools like run_simulation or get_opt_result.

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 a clear usage instruction: 'Use this when the user asks a conceptual... question.' It also mentions covering the distinction between optimization and simulation, implying when not to use this tool, but does not explicitly name sibling alternatives for simulation. Still, it provides good contextual guidance.

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