give_feedback

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

With no annotations, the description carries the full behavioral burden and discloses the key side effect: text is written to a local log only and never transmitted elsewhere. It does not cover log location, permissions, or return behavior, but the critical data-handling trait is stated.

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 two efficient sentences. The primary purpose and trigger conditions are front-loaded, and the second sentence adds essential behavioral context without waste.

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?

For a simple two-parameter feedback tool with no output schema and no annotations, the description covers purpose, usage triggers, and data handling adequately. It could be slightly more complete by clarifying whether feedback should also be given when verify finds issues, but the core context is sufficient.

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%, and the schema already documents both context and message thoroughly. The description adds no parameter-specific syntax, format, or constraints beyond what the schema provides, so the baseline of 3 is appropriate.

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 states a specific verb and target: reporting a problem to the operator. It also specifies the exact triggering conditions tied to the sibling tool solve, making it easy to distinguish from computational siblings like solve, verify, and poly_roots.

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?

It clearly says when to use the tool: when solve hits a dead end, returns an error, or produces something believed wrong. It does not state when not to use it or name explicit alternatives, but the contextual guidance is strong and actionable.

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