get_ai_engineering_report

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

No annotations are provided, so the description carries full burden. It declares read-only, explains attribution logic, parameter behavior, and the return of a 'unit' field. It lacks details on rate limiting or full output structure, but overall 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?

The description is well-structured, starting with a value proposition, then mechanism, parameter details, prerequisites, and example triggers. 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?

For a read-only report with 3 simple parameters and no output schema, the description covers purpose, parameters, prerequisites, and triggers. It provides a sample output line but does not fully specify the result structure, which is a minor gap.

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 0%, so description compensates. It explains the 'unit' parameter in detail (pr/commit/auto) and repos format. However, the 'days' parameter is only implied via 'date range' and not explicitly described, leaving a gap.

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 purpose: attributing AI-coded work to models/tools, sizing by diff, and joining spend. It provides a concrete example line, distinguishing it from sibling tools like get_ai_kpis or get_ai_spend_monitor.

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 specifies prerequisites (GITHUB_TOKEN, GITHUB_ORGS or explicit repos) and lists good trigger phrases. It does not explicitly contrast with alternatives, but the context is sufficiently clear for appropriate use.

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