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

Annotations mark this as read-only and non-destructive, and the description adds important behavioral context: it returns engine output with event counts, tank-level trace, and cycle summary. The ANTI-FABRICATION note telling the agent to quote verbatim and not recall from training data is a valuable disclosure 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 compact: two main sentences plus a brief anti-fabrication note. It front-loads the core purpose and returns structure without waste. Every sentence earns its place, including the critical anti-fabrication instruction.

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?

With no output schema, the description compensates by listing return components (event counts, tank-level trace, cycle summary). Given the tool's moderate complexity and strong annotations, the description provides sufficient context. It doesn't mention the parameter but the schema handles that.

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 description coverage is 100% for the single simulation_minutes parameter, which already includes default and range. The description adds no parameter-specific detail, but the schema fully covers it, so the baseline score of 3 is appropriate.

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 runs the Fast-Slow Drain (FSD) demo with a specific citation (Damiron-Nastasi 2008) and identifies its purpose as the canonical DRS-vs-DES event-count demonstration. This specific verb+resource combination distinguishes it from sibling demo tools like run_hamburger_duo or run_valdez_tanker.

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 clearly implies when to use the tool: when you need the canonical demonstration comparing DRS and DES event counts. While it doesn't explicitly name alternatives or exclusion criteria, the context is strong enough to guide selection among sibling run_* tools.

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