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

Beyond the readOnlyHint annotation, it discloses the key behavioral quirk: the response has mock: true, uses a canned body, and never dials the target. It also explains that a 200 only proves reachability, not delivery, which is essential for correct interpretation.

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 three focused sentences and each one earns its place: what it returns, how to use it, and what its mock behavior implies. The most important caveat is spelled out rather than implied.

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 single-parameter, read-only template generator with no output schema, the description fully covers the essential context: what the output is, how it should be consumed, and what not to assume about its behavior.

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 only parameter, lineKey, is already fully described in the schema with an example, so schema coverage is 100 percent. The description does not add extra meaning about the parameter, which is acceptable but also not a differentiator.

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 with a specific action and resource: 'Return a canonical minimal-valid endpoint JSON for a fresh line.' This clearly identifies the tool as a template generator and distinguishes it from endpoint CRUD tools like create_endpoint or get_endpoint.

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 an explicit workflow: POST the result verbatim to create_endpoint, then customise. It also tells the agent when the result should not be trusted as delivered: set mock to false and provide a real target URL first.

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