lupa_run_tests

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

No annotations are provided, so the description must fully convey behavioral traits. It states the tool returns structured JSON but omits details about side effects (e.g., file modification, console output), prerequisites (e.g., required project setup), error behavior, or destructive potential. The description is insufficient for an agent to fully anticipate the tool's runtime 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 two sentences with no wasted words. The first sentence states the core action and output, and the second provides a concrete usage example. Ideal conciseness for a tool with moderate complexity.

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 five parameters (mostly filters) and no output schema, the description is too sparse. It does not explain how filters combine (AND/OR), the structure of the JSON result, error handling for invalid configPath, or prerequisites (e.g., project initialization via lupa_init). These gaps make the tool less predictable for an AI agent.

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 schema already documents all five parameters. The description adds no parameter-level context beyond what the schema provides. Per guidelines, this yields a baseline score of 3.

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 action ('Run Lupa tests'), the output format ('structured JSON results'), and a primary use case ('identify failing tests'). It effectively distinguishes from siblings like lupa_init, lupa_list_test_files, and lupa_list_tests by specifying execution rather than initialization or listing.

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 provides a clear usage context ('Use this to identify failing tests') but does not explicitly state when not to use it or mention alternative tools for other purposes. The context signals and sibling names imply differentiation, but the description lacks explicit exclusion guidance.

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