math_batch

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

No annotations are provided, so the description carries the behavioral disclosure burden. It discloses parallelism, concurrency limited by CPU count minus one, result ordering, and the maximum call count. It does not explain per-call failure behavior, but the required input semantics and execution guarantees are reasonably covered.

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 and front-loaded: the first sentence states the core purpose, and subsequent sentences add only necessary operational constraints. Every sentence earns its place, with no filler or repetition of schema content.

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 generic batching tool, the description fully covers how to construct calls, limits, ordering, and execution semantics. Since an output schema exists, return-value details are already structured and do not need to be repeated in the description.

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 schema already documents the single 'calls' parameter with 100% coverage, describing the array of objects and the required 'name' and optional 'arguments' fields. The description restates this structure without adding new parameter-level detail, so it meets the baseline but does not elevate it.

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 opens with a specific verb and resource: 'Run multiple tools in one request,' and immediately clarifies the input shape as a list of objects with 'name' and optional 'arguments'. This distinguishes it clearly from the sibling tools math_ls, math_man, and math as the batching/composite tool.

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 clearly establishes the batching use case and provides operational constraints such as the 64-call limit and parallel execution. It does not explicitly name alternatives or state 'use this instead of individual calls,' but the context is clear enough for an agent to select it when multiple calls need to be grouped.

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