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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond annotations: it explains the coverage_level values ('full', 'basic', 'none') and what they mean, how analyzed_count and preliminary_count split total_restaurants, and that highlights with coverage_level 'basic' carry a preliminary_band instead of a grade. It also explains the not_found case. This is rich behavioral disclosure that goes beyond the annotations. The only minor gap is not describing the exact response structure, but the output schema exists and covers that.

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 a single paragraph but packs a lot of information efficiently. It front-loads the core purpose and outputs, then covers usage guidance, coverage semantics, and edge cases. It's slightly dense but every sentence earns its place. The only minor deduction is that the coverage explanation could be broken into clearer sentences, but it's not bloated or redundant.

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?

Given the tool's complexity (coverage levels, counts, preliminary bands, not_found case) and the fact that an output schema exists, the description is remarkably complete. It explains all the non-obvious behaviors an agent needs to interpret results correctly: what each coverage_level means, how counts split, what preliminary_band implies, and the not_found case. The single parameter is fully documented in the schema. Nothing critical is missing for an agent to call this tool correctly and interpret its output.

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 100%, so the schema already documents the 'area' parameter well, including examples and disambiguation guidance. The description adds value by framing the parameter in the context of the tool's purpose (area-level context) and reinforcing the disambiguation advice with the example 'Shoreditch, London'. Since the schema does the heavy lifting, a baseline of 3 applies, but the description's reinforcement of the comma-disambiguation pattern and the example queries earn a 4.

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's purpose: 'Get aggregate dining statistics for a neighborhood, city, or region' and lists specific outputs (cuisine breakdown, grade distribution, price range, top neighborhoods, highlighted restaurants). It distinguishes itself from siblings by explicitly stating it is NOT for finding a specific restaurant or personal recommendation, and names the alternative (recommend tool) for that use case.

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 provides explicit when-to-use guidance: 'Use this for area-level context' with an example query, and when-not-to-use: 'NOT for finding a specific restaurant or getting a personal recommendation.' It also names the alternative tool ('use the recommend tool instead') and gives a concrete example of when to use that alternative. This is exemplary usage guidance.

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