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

Annotations already declare readOnly, idempotent, non-destructive and closed-world behavior. The description adds information not in the annotations: the content is static text with no computed figures, so repeated calls with different intent will not yield new output — a genuinely useful disclosure for an agent deciding whether to invoke it again.

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?

One sentence, front-loaded with the resource, then the topic list, then the routing note about run_simulation. No filler and no redundancy with the title.

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-parameter, zero-output-schema documentation tool, the description covers what the content is, that it is static, and where to go for actual results. There is nothing an agent needs in order to call it correctly that is missing.

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 takes zero parameters, so the baseline is 4; there are no inputs whose semantics the description could clarify. It does implicitly confirm that no arguments are needed to select a topic.

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 names the resource (supply chain simulation primer) and enumerates the exact topics covered: when to simulate vs optimize, how a discrete-event network model works, which numbers the engine reports, and the limits of a single run. It also distinguishes itself from the sibling that produces numbers by pointing to run_simulation.

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?

It gives a clear condition — read this for concepts, call run_simulation for figures — so the agent knows when this tool is the right choice versus the simulation runners. It does not address the other explanatory siblings (explain_optimization, explain_greenfield, get_sc_theory), leaving that routing to be inferred.

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