get_test_results_summary

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

No annotations are provided, so the description carries full burden. It discloses that the tool uses GraphQL and does not return error messages. While it covers the main behavioral trait (what is omitted), it could be slightly more detailed about the scope or performance implications, but overall it is 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?

Three sentences that are dense with information. No wasted words. Purpose is front-loaded, then differentiation, then usage pointer.

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 the complexity (5 params, 1 required, output schema exists), the description fully explains what the tool returns and what it doesn't. It provides sufficient context for an AI agent to know when to call this tool and what to expect.

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% with all 5 parameters described. The description adds substantial context beyond the schema: recommended ranges for limit, source of task_id, meaning of execution, recommendation for failed_only, and explanation of bearer_token override.

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 gets test result metadata (test names, pass/fail, durations, Parsley URLs) and explicitly says what it does NOT return (actual error messages). It distinguishes itself from the sibling tool get_test_results_detailed.

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?

Provides explicit guidance: 'For the raw test log content with error pattern analysis, use get_test_results_detailed instead.' Also instructs to 'Use task_id from get_patch_failed_jobs results', which clarifies the prerequisite and context of use.

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