Kagan - AI Orchestration Layer

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool's role in a workflow and specifies verdict values ('PASS' or 'FAIL'), it lacks details on permissions, side effects, error handling, or what happens after reporting. For a mutation tool (implied by 'report' and setting verdicts) with zero annotation coverage, this is a significant gap in transparency.

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 highly concise and well-structured with three sentences. The first states the purpose, the second provides workflow guidance, and the third clarifies parameter constraints. Every sentence adds essential information without redundancy or fluff, making it easy to parse.

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 the tool's complexity (4 parameters, mutation implied, no annotations, no output schema), the description is moderately complete. It covers purpose, usage guidelines, and some parameter semantics, but lacks behavioral details like side effects, error conditions, or return values. For a tool in a review workflow, more context on consequences would be beneficial, but the basics are addressed.

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%, so the schema provides no parameter descriptions. The description adds some semantic value by explaining that 'verdict must be 'PASS' or 'FAIL'' and 'reason is a one-line justification,' which clarifies two parameters. However, it doesn't explain task_id or criterion_index, leaving half the parameters without context. The description partially compensates but doesn't fully address the coverage 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 specific action ('Report the AI review verdict') and the resource ('for a single acceptance criterion'). It distinguishes this tool from siblings like review_approve and review_reject by specifying its role in the review workflow. The verb 'report' is precise and the scope is well-defined.

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 explicit usage instructions: 'Call this once per criterion during review, BEFORE calling review_approve or review_reject.' This clearly defines when to use it (per criterion during review) and its sequencing relative to alternatives (review_approve/review_reject). No misleading or missing guidance is present.

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