generate_load

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the tool blocks for a duration ('Blocks for roughly iterations * interval_seconds seconds'), is synchronous ('this tool is synchronous by design'), and provides per-iteration results ('the caller sees per-iteration results'). However, it lacks details on error handling, rate limits, or performance impacts, which are important for a load-generation tool.

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 appropriately sized and front-loaded: the first sentence states the core purpose, and subsequent sentences add crucial behavioral context without redundancy. Every sentence earns its place by clarifying usage and limitations, making it efficient and well-structured for an AI agent.

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 the tool's complexity (load simulation with multiple parameters), no annotations, and an output schema (which reduces need to describe returns), the description is moderately complete. It covers purpose, behavior, and usage guidelines but lacks details on parameters and advanced behavioral aspects like error handling. This leaves gaps for effective tool selection and invocation.

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 0%, so the description must compensate for undocumented parameters. It mentions 'iterations' and 'interval_seconds' in the blocking context, adding meaning beyond the schema. However, it doesn't explain 'profile', 'endpoints', or 'options', leaving three of the five parameters without semantic clarification. This partial coverage is insufficient given the low schema coverage.

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's purpose: 'Repeatedly run a mimic profile to simulate sustained traffic.' It specifies the verb ('run'), resource ('mimic profile'), and goal ('simulate sustained traffic'), which distinguishes it from siblings like 'mimic_tool' (likely a single run) or monitoring tools. However, it doesn't explicitly differentiate from all siblings, such as 'send_metric' or 'send_trace', which might also involve traffic 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?

The description provides explicit usage guidance: it states when to use this tool ('to simulate sustained traffic') and when not to use it ('For true background loops use an external scheduler — this tool is synchronous by design'). It contrasts with alternatives by noting the tool is synchronous and blocks, implying that asynchronous or background tasks should use other methods, though it doesn't name specific sibling alternatives.

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