discrete_log_naive

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

No annotations are provided, so the description carries full burden. It mentions the method is 'naive' which implies inefficiency for large inputs, but doesn't specify computational complexity, time/space requirements, or what happens when no solution exists (e.g., returns null, error). For a computational tool with no annotations, this leaves significant behavioral gaps.

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 extremely concise—a single sentence plus domain/category tags. Every word serves a purpose: it states the problem, method, and context without redundancy. It's front-loaded with the core functionality, making it efficient for quick understanding.

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 complexity (solving discrete logarithms), lack of annotations, 0% schema coverage, and no output schema, the description is inadequate. It doesn't explain the algorithm's behavior, error conditions, performance characteristics, or output format. For a mathematical solver with four parameters, this leaves too much undefined for reliable agent use.

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 0%, so the schema provides no parameter documentation. The description only implicitly defines g, h, n via the equation g^x ≡ h (mod n), but doesn't explain the optional max_exp parameter at all. It adds minimal semantic value beyond what's inferable from the equation, failing to compensate for the schema's lack of descriptions.

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's purpose: 'Solve discrete logarithm g^x ≡ h (mod n) using naive method.' It specifies the mathematical operation (solve discrete logarithm), the method (naive), and the domain/category context. However, it doesn't distinguish this tool from potential sibling discrete logarithm methods (like baby_step_giant_step which appears in the sibling list), so it's not a perfect 5.

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 provides minimal usage guidance. It mentions the mathematical domain (arithmetic) and category (modular_arithmetic), but offers no explicit guidance on when to use this naive method versus alternatives (like baby_step_giant_step for the same problem), nor any prerequisites or limitations. The agent must infer usage from the method name alone.

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