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

Annotations already indicate a read-only, non-destructive operation. The description adds meaningful behavioral context by explaining that pre-computed embeddings are used and that similarity combines semantic and structured attributes, which implies a dependency on precomputed data. No contradiction with annotations.

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 two sentences, front-loads the core purpose, and adds a second sentence to clarify the algorithm without unnecessary fluff. Every word earns its place.

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 has a rich schema (100% coverage) and an output schema, so the description does not need to explain return values. It covers the algorithm and key parameters, though it does not mention potential edge cases like what happens if no embeddings exist or if campground_id is invalid. Overall adequate for the complexity.

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% with detailed parameter descriptions for result_limit, campground_id, max_distance_km, semantic_weight, and structured_weight. The description adds only a brief conceptual link between the embedding types and the weight parameters, but the schema already explains the meaning and defaults, so the value added is marginal.

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 states a specific verb ('find') and resource ('campgrounds similar to a given campground'), and distinguishes itself from sibling tools like search_campgrounds by detailing the method ('vector embedding similarity'). It is clear and specific.

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 context (when you have a campground and want similar ones) but does not explicitly contrast with search_campgrounds or other alternatives. There is no 'when not to use' guidance, so it falls short of explicit guidance.

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