evaluate_model_inference

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

Description discloses the metrics computed and the pass/fail verdict, but lacks details on side effects, configuration of thresholds, error handling, or data persistence. Since annotations are absent, more context would be beneficial for a score above 3.

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?

Two focused sentences with no redundancy; first sentence defines core purpose, second details outputs. Efficient and well-structured.

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?

Description covers core purpose and main outputs but omits details on return format, configuration of thresholds, and handling edge cases. Given no output schema, the description could be more complete.

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 covers all parameters with descriptions. The description's mention of 'enterprise performance thresholds' hints at the min_recall/min_precision parameters but doesn't add new semantic information. At 100% coverage, baseline 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?

Description explicitly states the tool's function (evaluates inference accuracy) and lists computed metrics (precision, recall, F1, AUC, pass/fail), clearly distinguishing it from sibling tools like audit_data_quality or detect_bias.

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?

Description provides no guidance on when to use this tool over siblings. It only describes the tool's function without contextualizing against alternatives or specifying prerequisites.

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