algebraic_equiv

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

No annotations are present, and the description only provides a caveat about symbolic identity not addressing numerical concerns. It does not disclose return type, error handling, or handling of invalid inputs, leaving behavior partially transparent.

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 concise, using two short sentences plus a caveat. No extraneous words, and the example effectively illustrates the intent.

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 absence of parameter descriptions and annotations, the description offers some context (example, caveat) but lacks details on expected return values, error conditions, or variable handling. It is adequate for a simple tool but not fully comprehensive.

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 provides no descriptions for 'a' and 'b'. The example implies they are strings representing algebraic expressions, but the exact syntax (e.g., Python vs mathematical notation) is not specified, requiring the agent to infer format from the example.

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 checks algebraic equivalence of two expressions, with a concrete example '(a*b)/c vs a*(b/c)' and explicitly notes it ignores numerical issues like floating-point rounding.

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 caveat implies when not to use it (e.g., for numerical equality), but it does not explicitly reference sibling tools like 'compare_threshold' or 'solve_expression' for alternative use cases. Use is implied rather than explicitly guided.

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