find-counterexample

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 explains the tool's mechanism ('Searches for a model satisfying premises ∧ ¬conclusion') and outcome behavior ('If found, proves the conclusion doesn't logically follow'), which is valuable context beyond basic functionality. However, it doesn't mention performance characteristics like computational limits or error handling.

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 well-structured with clear sections (purpose, usage guidelines, example, mechanism), uses bullet points effectively, and every sentence adds value. It's appropriately sized for a tool with multiple parameters and sibling alternatives.

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 logical reasoning complexity and lack of output schema, the description provides good contextual coverage: purpose, usage guidelines, example, and operational mechanism. It could be more complete by explaining the format of returned counterexamples or error conditions, but it's largely adequate for the agent's needs.

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 100%, so the schema already documents all parameters thoroughly. The description adds minimal parameter-specific information beyond the schema (e.g., it implies 'premises' and 'conclusion' are logical formulas in the example). This meets the baseline for high 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 explicitly states the tool's purpose: 'Find a counterexample showing the conclusion doesn't follow from premises.' It uses specific verbs ('find', 'showing') and clearly distinguishes it from the 'prove' sibling tool, making the purpose unambiguous and differentiated.

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 includes explicit 'When to use' and 'When NOT to use' sections, providing clear guidance on when to select this tool versus the 'prove' alternative. This directly addresses sibling tool differentiation and usage context.

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