evaluate_decision_table

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

With no annotations, the description carries full burden. It discloses that the tool checks completeness and determinism, and hints at mode behavior (first_match default). However, it does not describe error handling, side effects, or what happens when conditions are not met.

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 concise sentences, front-loaded with the core action, followed by additional checks. No wasted words.

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?

No output schema provided, and the description does not describe the return value or structure (e.g., match results, error messages). Given the complexity of nested objects and enums, this is a significant omission.

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 provides 100% coverage with descriptions for each parameter. The description adds no additional semantic value beyond what is in the schema; it only mentions general behavior without elaborating on specific parameters.

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 clearly states the main action: evaluating a matrix of decision rules against input context, and explicitly mentions additional checks for completeness and determinism. This distinguishes it from sibling tools like evaluate_boolean or evaluate_json_logic.

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?

No guidance on when to use this tool versus alternatives. Siblings like analyze_state_machine or simulate_state_machine are not mentioned, and no when-not-to-use advice is given.

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