predict_regression

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

With no annotations, the description must fully disclose behavioral traits. It implies a read operation (scoring risk) but does not explicitly state side effects, idempotency, or authorization needs. Missing details about whether past data is fetched or modified.

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 single sentence that front-loads the core purpose. It is concise but could benefit from structured breakdown for clarity.

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?

The description lacks completeness for a scoring tool with no output schema and no annotations. It does not describe the return value (e.g., a numeric score or category), limiting the agent's understanding of 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 description coverage is 0%, and the description adds no meaning to the parameters beyond their names. 'file_path' and 'change_description' are not elaborated, and the basis (past bugs, test failures, constraint violations) is not mapped to input fields.

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 verb (score risk) and resource (proposed change to a file) with specific inputs (past bugs, test failures, constraint violations). It distinguishes from siblings like 'predict_test_failures' and 'simulate_change' by focusing on regression risk, though not explicitly contrasting.

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?

No guidance is provided on when to use this tool versus alternatives. It does not mention when it is appropriate or inappropriate, nor does it reference sibling tools for comparison.

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