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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently states that it returns graph edges with line names and importantly discloses a limitation: it does not indicate whether specific trains stop. This is valuable context beyond a simple 'returns neighbors'. It does not mention side effects or read-only nature, but for a query tool this is acceptable.

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 with no waste. It front-loads the core purpose (returns adjacent stations with line names) and then adds the important caveat. All information 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 description is complete for a simple one-parameter tool with no output schema. It explains what is returned (adjacent stations, line names) and a key limitation. It does not detail the exact response structure (e.g., objects with station/line fields) or handle error cases, but for a tool this simple, the description is adequately complete.

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 input schema fully documents the parameter with a description ('駅名（例: 渋谷）'), so schema coverage is 100%. The description does not add additional parameter-specific meaning; it only elaborates on the output. Given the high schema coverage, a baseline of 3 is appropriate.

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 a specific verb ('返す' - returns), resource (adjacent stations on the track), and adds context (graph edges, line names). It also distinguishes itself from a generic 'neighbors' by specifying it's track-level adjacency, not train stops, which differentiates it from the sibling 'subgraph' tool.

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 context that it returns track adjacency but does not explicitly state when to use this tool versus the sibling 'subgraph'. There is no direct comparison or 'use this when' guidance. The note about train stops is a limitation, not usage guidance, so the usage is implied rather than stated.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the result is raw/unprocessed ('生グラフ...そのまま返す'), includes edge types, and requires the caller to do its own traversal. These are useful behavioral cues, but it does not disclose return format details, whether the result has limitations beyond the schema's stated max radius, or any other operational 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?

Two compact sentences with zero filler. The purpose is front-loaded in the first sentence, and the second adds usage guidance that differentiates from the sibling. Each sentence earns its place, though the overall lean length trades off some behavioral depth.

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?

With no output schema and no annotations, the description is the only source for return expectations. It conveys that the result is a typed edge list within a radius, which is adequate for a simple two-parameter read tool. However, it omits the concrete output format and any structural details of the graph, leaving the agent to infer what 'raw graph' means in practice.

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%: both radius (default 2, max 4) and station are already documented in the input schema. The description reinforces the radius concept ('半径N駅') but adds no meaning beyond what the schema already provides. Baseline 3 is correct when the schema carries the parameter documentation burden.

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+resource: it returns the raw graph (edge list with type) within radius N stations around a station. It explicitly distinguishes itself from the sibling 'neighbors' by noting it serves structural queries that neighbors cannot handle and requires self-traversal. An agent can differentiate subgraph from neighbors without opening the schema.

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 second sentence gives clear when-to-use guidance: for structural queries that neighbors doesn't suffice for, read this and traverse it yourself. It references the alternative tool and the condition that selects it. However, it does not explicitly state the inverse (e.g., 'use neighbors for simple neighbor lookups'), leaving that exclusion implied rather than stated.

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