board_ascii

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

No annotations are provided, so the description carries the burden. It discloses that the output is human-oriented ASCII from White's perspective, and that it differs from the JSON representation. While it does not explicitly state 'read-only' or 'no side effects', the verb 'Return' clearly implies a read operation, and the tool's purpose is well explained.

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 compact and front-loaded. The first sentence states the core purpose, and the bullet points provide necessary context and usage guidance without redundancy. Every sentence contributes value.

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?

This is a simple tool with no parameters and a clear output. The description covers its purpose, perspective, use case, and points to an alternative for a different use case. The output schema exists and handles return-value details, so no further completeness issues.

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?

The tool has zero parameters, so the schema is empty and 100% covered. Per the rubric, baseline is 4. The description adds no parameter-specific information, but none is needed.

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 returns an ASCII representation of the board from White's perspective. It distinguishes the tool from get_status by noting that the JSON map is preferred for model reasoning, effectively differentiating the two sibling tools.

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 explicitly states when to use this tool (human-oriented display in UIs, logs, or chat) and when not to use it (for model reasoning, prefer get_status().pieces). This provides clear guidance on choosing between board_ascii and the alternative.

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