get_testcase_details

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 reveals key behaviors: the two identification methods, the requirement to pair testcase_name with testrun_id due to duplicate names, the detailed return payload, and the effect of steps_filter='failed_only' in stripping passing steps. This is strong disclosure, though it omits potential rate limits or auth requirements, which are not critical for a read operation.

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 tight paragraph that front-loads the purpose, then efficiently covers identification methods, return contents, the filter option, and a usage example. Every sentence adds necessary information without redundancy, making it concise and well-structured.

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?

Despite having 12 parameters and no output schema, the description provides a complete overview of the main workflows: how to identify a test case, what data to expect, and how to narrow results. It does not cover deprecated parameters or history settings, but those are fully documented in the schema. The description is sufficient for correct tool selection and invocation.

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 description coverage is 100%, so the baseline is 3. The description adds valuable semantics beyond the schema by explaining when testcase_id can be used alone versus when testcase_name must be combined with testrun_id, and by clarifying the effect of steps_filter. This helps the agent correctly assemble parameters, raising the score above baseline.

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 tool's purpose with a specific verb and resource: 'Get detailed information about a specific test case.' It lists the returned data types (error message, code snippet, logs, artifacts), which helps distinguish it from generic list tools. However, it does not explicitly differentiate itself from sibling tools like debug_testcase, so it misses the top score for sibling distinction.

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 gives practical usage context: 'Use this to debug why a test failed or understand how it executed' and includes a concrete example. It does not mention alternatives or exclusions, but the context is sufficiently clear for an agent to know when this tool is appropriate.

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