get_test_history

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

With no annotations, the description conveys that the tool reads archived snapshots (no destructive actions) and returns summary data. It specifies the fields returned and the adjustable limit. It could be more explicit about being read-only, but the context implies no side effects.

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 concise (4 sentences), front-loaded with the tool's action and output, followed by use cases, parameter info, and an alternative tool. Every sentence adds value without redundancy.

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 output schema, the description explains the return fields and covers three distinct use cases. It also mentions the adjustable limit and points to a related tool for further analysis. For a single-parameter read tool, this provides sufficient context.

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?

The schema already describes the 'limit' parameter with default and range. The description adds usage guidance (e.g., 'for long-term flake analysis, suggest 30+'), which provides meaningful context beyond the schema.

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 clearly states the tool traverses history snapshots and returns per-run summaries with specific fields (timestamp, total, passed, etc.). It differentiates from siblings by specifying use cases (flake analysis, speed regression, coverage trends) and directs to get_optimization_plan for actionable suggestions.

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?

Explicitly lists when to use (flake analysis, speed regression, coverage trends) and provides an alternative: 'if you want actionable suggestions, call get_optimization_plan'. This gives clear context for tool selection.

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