recommend_parking

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

With no annotations, the description is the only source of behavioral disclosure. It usefully specifies that each recommendation must include availability_summary, distance_summary, expected wait time, and a KakaoMap link, but it does not mention data sources, freshness, real-time semantics, or that this is a read-only operation.

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: the purpose sentence comes first, followed by a concise output-format requirement. The example summaries are useful rather than redundant, though the response-formatting sentence could have been slightly tightened.

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 five parameters, no output schema, and no annotations, so the description alone must make the tool safely callable. It gives a good outline of the output but omits semantics for several inputs and fails to distinguish when to use this instead of find_parking_lots or estimate_wait_time.

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 description must compensate for the five parameters. It only maps the first sentence's criteria to possible priority choices and leaves radius_m, expected_parking_hours, and current_queue_count_by_lot unexplained.

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 function: recommend parking lots around a destination using distance, wait time, fee, and beginner-friendliness criteria. It uses a specific verb and resource, and the 'recommend' framing broadly separates it from sibling tools like find_parking_lots, though it does not explicitly name the distinction.

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?

Usage is implied from 'recommends parking lots around the destination' — an agent can infer it should be called when a user wants a parking recommendation. However, there are no explicit when/when-not conditions or references to sibling tools such as find_parking_lots, estimate_wait_time, or analyze_parking_congestion.

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