get_run_details

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

With no annotations, the description carries the full burden and exceeds expectations. It discloses batch limits (max 20), lazy generation of AI insights, status values ('processing'/'not_generated'), and the meaning of 'unavailable' sections including transient vs terminal upstream status codes. This gives the agent accurate expectations about side effects and retry behavior.

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 longer than typical but well-structured: main purpose first, then AI insights specifics with actionable polling guidance. Every sentence adds operational value, though the AI Insights section is dense and could arguably be split, but it is necessary for correct invocation.

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?

With no output schema and no annotations, the description compensates thoroughly. It lists the returned data categories and details the structure of AI Insights, including failure categorization, clusters, cards, table, and summary. This provides a complete mental model for invoking the tool and interpreting results.

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%, so baseline is 3, but the description adds meaning beyond the schema: it clarifies that include_ai_insights requires a single testrun_id (not counter/batch) and explains the lazy-generation behavior. It doesn't fully resolve the counter vs testrun_id ambiguity, but it enriches the understanding of parameter usage.

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 function: 'Get detailed information about test runs' and enumerates the specific data returned (statistics, suites, cases, git metadata, error details). It distinguishes itself from siblings like get_run_error_clusters and get_ai_insights by focusing on the full run details and batch operations.

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 explicitly says 'Use this to analyze test execution health or debug specific failures.' It also provides when-not-to-use guidance by directing users to poll get_ai_insights when AI insights are still processing, rather than re-calling this tool. This clarifies the boundary between the two tools.

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