check_testrail_auth

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

No annotations provided, so description carries full burden. It details the lightweight call (get_priorities), the return dict structure with ok, config, success/failure fields, and even includes error_class and hints. Fully transparent.

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?

Description is well-structured with clear sections and bullet points. Slightly verbose but not overly so; every sentence adds value. Could be trimmed, but still effective.

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 no parameters and an output schema (though not provided in input, description explains it), the description is complete. It covers purpose, usage, behavior, and return values thoroughly.

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?

Tool has zero parameters. According to guidelines, baseline is 4. The description does not need to add parameter information.

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?

Description explicitly states it verifies TestRail credentials and provides a structured diagnosis. The verb 'Verify' and resource 'TestRail credentials' are specific. It clearly distinguishes itself from sibling tools like run_testrail_command or search_test_cases.

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?

Lists explicit scenarios when to use: user reports server issues, auth errors from other tools, or before longer workflows. Lacks explicit when-not-to-use, but the context is clear and sufficient for an AI agent.

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