pub_eval_score

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

No annotations are provided, so the description carries the transparency burden. It discloses the sign convention ('Positive favors the mover, negative favors opponent'), the required player condition for dict board inputs, and supported input formats. It does not exhaustively describe errors or side effects, but none are expected for a pure scoring function.

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 with a one-sentence summary, followed by a terse Args/Returns block. Every line adds unique information; there is no repetition of schema fields or annotation data.

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 simple two-parameter schema and the presence of an output schema, the description covers purpose, parameters, and result sign. The only notable gap is lack of explicit guidance on alternatives, though 'fast heuristic' implies the usage context. This is sufficient for an agent to invoke the tool correctly.

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 schema has no property descriptions (0% coverage), so the description compensates by explaining that board_input accepts 'any supported format' and that player is required if board_input is a boardState dict. It stops short of enumerating all supported formats or explaining player optionality for non-dict inputs, but adds meaningful semantics beyond the raw schema.

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 opens with 'Compute a fast heuristic evaluation score using GNUBG's public (non-neural) model' — a specific verb with a clear resource. This distinguishes it from sibling tools like best_move, moves, and equities_value by naming the evaluation model and heuristic nature.

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 phrase 'fast heuristic' clearly signals when this tool is appropriate, implying quick evaluation rather than full rollout. It does not explicitly name alternative tools or when-not-to-use scenarios, but the provided context is clear enough.

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