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

With no annotations provided, the description fully carries the behavioral disclosure burden. It details what is returned, including the internal-comment visibility rule, autoRule/autoWording semantics, and the found=false behavior for non-support issues. This is thorough for a read operation, though it doesn't cover permissions or error formats.

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 a single dense sentence that packs in many relevant details without excessive verbosity. It front-loads the main purpose and each clause adds value, though it could be structured into clearer sections for easier parsing.

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 there is no output schema and no annotations, the description does an excellent job of describing the return payload structure and edge-case behavior. It lacks explicit error-handling details but is quite complete for a read-only tool.

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% for the single 'key' parameter, which is well-documented with an example. The description adds no additional parameter-specific guidance, so a baseline score of 3 is appropriate since the schema does the heavy lifting.

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 clear verb ('Read') and resource ('who is behind a support request'), then enumerates the specific data returned (requester info, auth state, context, visible comments). This clearly distinguishes it from generic get_issue or get_comments, even without naming siblings.

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 implicitly defines its scope by stating it reads support requests and returns found=false for non-support issues, which guides when to use it. However, it does not explicitly compare with alternatives like get_issue or list_support_queue, so some inference is required.

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