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. It discloses key behavioral traits: the tool performs checks (non-destructive), returns pass/warn/fail statuses, and includes specific checks for GitHub (CLI installation and authentication). However, it lacks details on error handling, rate limits, or permissions required, leaving some gaps 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by return values and parameter guidance. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 moderate complexity (1 parameter, no annotations, but with an output schema), the description is mostly complete. It explains the purpose, usage, and parameter semantics adequately. Since an output schema exists, it need not detail return values. However, it could improve by mentioning prerequisites or common use cases, slightly reducing completeness.

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 description must compensate. It adds meaningful semantics: the 'plugin' parameter specifies which plugin to check, with omission leading to checking all installed plugins. This clarifies the parameter's role beyond the schema's basic type and title. However, it does not detail format constraints or examples, slightly limiting its utility.

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 with specific verbs ('Check if a plugin's external dependencies are satisfied') and resources ('plugin's external dependencies'). It distinguishes itself from sibling tools like plugins_sync or project-related tools by focusing on dependency verification rather than synchronization or project management.

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 clear context for when to use the tool: to verify dependencies for a specific plugin or all installed plugins. It includes an example for GitHub ('For github: verifies gh CLI is installed and authenticated'), which helps guide usage. However, it does not explicitly state when not to use it or name alternatives among siblings, preventing a perfect score.

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