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

It provides extensive context far beyond the `readOnlyHint: true` annotation, detailing the data model. It explains complex behaviors like how 'messages' are filtered (internal vs. requester-visible) and the specific meaning of `autoRule` and `autoWording` flags.

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 excessively long. It functions as a complete data dictionary for the response object, which is too much detail for the standard description field, sacrificing conciseness for exhaustiveness.

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 that there is no output schema, this description is 100% complete, as it exhaustively defines all the fields and behaviors of the data the agent expects to retrieve, which is the most critical information here.

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 only defines the 'key' parameter. Since schema description coverage is 100%, the baseline is a 3, and the description provides no additional semantic guidance on the 'key' parameter itself.

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 purpose: 'Read who is behind a support request'. While it doesn't name a specific sibling, the combination of 'get' and 'support request' makes the intent unambiguous within a ticketing system context.

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 by detailing exactly what specific data points are returned (e.g., requester name, autoRule flags, internal vs. visible comments), suggesting it is the correct tool for retrieving rich, internal support request data.

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