create_smart_issue

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

With no annotations, the description must disclose behavioral details. It only states 'AI-suggested' without explaining whether user-provided type/priority override the AI, how labels are handled (they aren't in the schema), or any permissions or side effects. The description also doesn't mention the return value.

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, efficient sentence with a clear verb-first structure. No fluff, but it could benefit from slightly more detail given the tool's 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?

The tool creates an issue with AI-suggested fields, yet the description omits essential context: no output schema, no annotations, no behavior explanation for labels (missing from schema), and no clarification of parameter roles. It is not complete enough for an AI agent to use confidently.

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 some nuance for type and priority (AI-suggested) but fails to clarify the required text and projectKey parameters, nor how optional parameters interact with the AI suggestions.

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 uses a specific verb 'Create' with the resource 'issue' and adds 'AI-suggested type, priority, and labels' which clearly distinguishes it from the sibling create_issue tool. The purpose is immediately clear.

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?

There is no explicit guidance on when to use this tool versus alternatives like create_issue or analyze_issue_text. The AI-suggested aspect implies a use case, but no direct when-to-use or exclusions are provided.

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