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

Annotations already supply readOnlyHint=true, and the description complements this by clarifying that counts are 'STATUTORY, from history notes — not observations' and by enumerating the return categories. This adds behavioral context beyond the structured hints, though it does not discuss pagination or exact limit behavior.

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 the core purpose in the first sentence. Every subsequent sentence earns its place: use case, return contents, chapter behavior, data provenance, and sibling alternatives. No filler or redundant restatement.

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 read-only aggregation tool with an output schema and no required parameters, the description is largely complete: it states scope, output summary, data semantics, and alternatives. The only gap is that top and since_year are not explicitly described, but their meaning is reasonably inferable and defaults make the tool callable without them.

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 description coverage is 0%, so the prose must explain parameters. It clearly explains chapter ('Omit chapter for a corpus-wide view') and links it to tool selection, but top and since_year are left to their names and defaults. The description partially compensates for the missing schema descriptions but not completely.

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 by defining the tool's purpose: measuring 'legislative churn' at the North Carolina statute chapter level, and specifies what is returned (amendment counts by year, most-amended sections, pending/repealed counts). It also separates itself from siblings by naming amendment_history and pending_changes as the tools for single-section and upcoming-text needs.

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?

It gives a concrete use case ('which parts of Chapter 14 keep changing'), tells the caller to omit chapter for a corpus-wide view, and explicitly names alternatives: 'For one section use amendment_history; for upcoming text use pending_changes.' This is direct routing guidance with no reliance on inference.

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