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

With no annotations available, the description carries the full burden. It states the calculation outputs but doesn't disclose return format, rounding behavior, or how inputs like thinkTimeMs and safetyFactor affect results. It provides a reasonable high-level behavior but lacks deeper edge-case or output details.

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 a single, well-front-loaded sentence that efficiently lists the tool's outputs and input basis. No filler or redundant phrasing; every word adds value.

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?

The tool has no output schema and no annotations, so the description must compensate. It lists output categories but doesn't specify the exact return structure, units, or how recommendations are derived beyond mentioning Little's Law. This leaves some practical uncertainty for an agent deciding whether to invoke the tool and how to interpret results.

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 already provides 100% description coverage, so the description doesn't need to add parameter meaning. It does reinforce the key inputs (target RPS/TPS and SLA response times) by mentioning them, but doesn't elaborate on optional parameters like thinkTimeMs or testDurationMinutes beyond schema defaults.

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 uses a specific verb 'compute' and clearly names the resource: workload model with concrete outputs (thread concurrency, pacing delays, ramp-up schedules, JVM heap). This distinguishes it from sibling tools like plan_distributed_testing or search_jmeter_docs, which serve different purposes.

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 clear context for when to use this tool: when you have target RPS/TPS and SLA response times and need to derive load parameters via Little's Law. It doesn't explicitly compare to alternatives or state exclusions, but the context is unambiguous enough for most scenarios.

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