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

Annotations already mark this as a safe read, and the description adds valuable context: an 'as of May 2026' date, a list of key states, and a data source. It does not describe the return format or edge cases, but the added context goes beyond the annotation baseline.

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 three sentences with a detailed example and cost estimate, making it informative but somewhat verbose. It front-loads usage scenarios rather than a direct purpose statement, which reduces conciseness.

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?

The tool is simple (one optional parameter, no output schema), and the description provides rich domain context about the regulatory patchwork. However, it does not specify what the response contains (e.g., a list of laws, summaries), which is a gap given the absence of an output schema.

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 fully documents the optional 'state' parameter with 100% coverage, so the description does not need to add parameter details. The description adds no parameter-specific meaning, but the schema is sufficient, earning a baseline 3.

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 positions the tool as covering US state AI legislation, with specific use cases like mapping compliance obligations and briefing teams. It distinguishes from siblings like get_colorado_ai_act_requirements by emphasizing the multi-state patchwork, though it never directly states what the tool returns.

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 explicitly says 'Use when' and lists three concrete scenarios, giving strong context for when to invoke this tool. It does not mention alternative tools or exclusions, so it stops short of full when-not guidance.

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