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

Annotations provide only false values, placing burden on the description. The description explains that feedback is read by the team and mentions validation constraints, but does not explicitly state side effects (e.g., data storage) or idempotency. It adds moderate value beyond annotations.

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, well-structured paragraph that front-loads the purpose and includes necessary guidance without excessive verbosity. It's concise enough for efficient reading.

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 feedback submission tool without an output schema, the description provides sufficient context: when to use, what to provide, and constraints. It lacks return value description but that's acceptable given the tool's nature.

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 each parameter. The description adds semantic value by advising low-friction format: 'provide goal, expected, and observed instead of a rationale field.' This helps agents construct effective feedback.

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: reporting problems back to the DexPaprika team when stuck or encountering unexpected behavior. It uses a specific verb ('report') and resource, and distinguishes it from sibling tools that are all data retrieval functions.

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 explicitly tells when to use the tool: when a tool gets stuck, returns unexpected results, lacks data, or behaves differently than documented. It also encourages submitting partial feedback and provides format guidance. While it doesn't state when not to use it, the context is clear.

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