symbolic_equal

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

With no annotations, the description carries the full burden of disclosure. It provides examples of equivalence behavior (e.g., trigonometric identity) and states that it returns whether expressions are equivalent, but it does not mention limitations, edge cases, or whether the check is purely read-only. This is adequate but not rich.

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 well-structured with a clear purpose, usage note, Args section, Returns section, and two illustrative examples. Every sentence earns its place and the text is front-loaded with the core behavior.

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 simple two-parameter comparison tool with an output schema, the description covers what the tool does, when to use it, parameter semantics via examples, and return type. The guidance about verify.py tools adds important context about alternatives, making the description complete enough for an AI agent to select and invoke the tool correctly.

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 has no descriptions for expr1/expr2 (0% coverage), so the description's Args block and examples must compensate. It adds only 'First expression' and 'Second expression,' which barely expands on parameter names, though the examples clarify the expected string format. This is minimal but sufficient for two obvious parameters.

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 'Check if two expressions are symbolically equivalent' with a specific verb and resource. It also distinguishes itself from more thorough verification tools by labeling itself as useful for 'quick verification' and directing users to verify.py tools for deeper checks.

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 explicitly says it is 'Useful for quick verification of derivation steps' and contrasts with 'For more thorough verification, use verify.py tools.' This gives clear when-to-use and when-not-to-use guidance, even though sibling tools are not individually named.

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