evaluate_json_logic

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

The description highlights 'deterministic' behavior and lists supported rule types, which is moderately transparent. However, without annotations, it fails to disclose error handling, side effects, or output format, leaving gaps for an agent.

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 sentences front-load the primary purpose and quickly enumerate supported features. Every word adds value; no fluff or repetition.

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 tool with no output schema, the description omits the return value (e.g., evaluated result). It covers inputs and supported operations but lacks outcome details, which is a gap for completeness.

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 coverage is 100%, so parameters are documented. The description adds no extra semantic detail beyond the schema examples. The mention of 'context dataset' and 'rule' aligns with schema but doesn't enhance meaning.

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 evaluates deterministic JSON Logic rules against a context dataset, specifying supported rule types (boolean, comparisons, math, conditionals, array). This distinguishes it from siblings like solve_sat or analyze_state_machine, which target different domains.

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 implies usage for evaluating JSON Logic rules but does not explicitly guide when to choose this tool over alternatives (e.g., evaluate_boolean for simpler boolean logic). No exclusions or context for misuse are provided.

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