prove_correctness

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

No annotations provided, so description must cover behavioral traits. It states it uses 'loop invariants or mathematical proofs' but does not disclose side effects, return format, required permissions, or whether it is read-only. For a correctness verification tool, more context is needed.

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?

Single sentence, efficient at 12 words, verb front-loaded. Slightly sparse but not wasteful; could be improved with brief context without losing conciseness.

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 presence of an output schema, description needn't detail return values. However, it lacks usage guidelines and behavioral transparency, making it barely adequate for a complex reasoning tool. Covers purpose but misses important decision-making context.

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 coverage is 100% for the single parameter 'problem', with a clear description. The tool description adds no extra meaning beyond the schema, so baseline 3 applies.

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 uses a specific verb ('Verify correctness') and resource ('an approach'), and names methods ('loop invariants or mathematical proofs'). It clearly distinguishes from siblings like find_bug (bug detection) or review_solution (code review) by focusing on formal proof techniques.

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?

No explicit when-to-use or when-not-to-use guidance. Does not mention alternatives or contrast with sibling tools like find_bug or generate_solution. The description is too generic to help an agent decide between this and other verification-related tools.

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