truth_table

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

With no annotations, the description carries full burden for behavioral disclosure. It only states the core action and gives examples, but does not explain how invalid expressions are handled, operator precedence rules, variable naming constraints, or the exact output format.

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, focused sentence that front-loads the primary action and includes relevant examples. Every word earns its place with no redundancy or filler.

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 a simple one-parameter interface and an output schema, so the description need not detail return values. However, the lack of usage guidance and behavioral transparency leaves some gaps, making it minimally complete but not 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?

The input schema has a single string parameter with 0% schema description coverage, so the description must compensate. It provides examples of acceptable boolean expressions ('a and b or not c', 'p xor q', 'a implies b'), which gives partial semantics, but it does not fully specify grammar, allowed operators, or variable rules.

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 a specific action ('Build the truth table') on a specific resource (a boolean expression), with illustrative examples. It distinguishes this tool from siblings like evaluate_expression or algebraic_equiv by focusing on truth table generation.

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 implies use when a truth table is needed but provides no explicit guidance on when to use it versus alternatives (e.g., evaluate_expression, simplify_expression). No exclusions or when-not-to-use conditions are mentioned.

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