ai_try_solve

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

With no annotations, the description bears full burden. It discloses the return value (solution or fallback message) but does not mention side effects (e.g., does it modify data?), required permissions, or what happens on failure beyond the message. The behavioral impact is unclear.

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 plus @param lines. The @param lines add little value and could be removed. It is not excessively long, but the Javadoc-style formatting is slightly verbose for the content provided.

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 no annotations and an output schema present, the description covers the return value but lacks detail on parameter formats or edge cases. With 4 required params, more guidance would improve completeness. The fallback message provides useful context for when the tool fails.

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 must compensate. The @param comments only repeat parameter names (e.g., 'The description of the issue') without adding meaning, allowed values, or format. With no enums or further details, the semantics are weak.

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 states the tool's purpose: 'try to solve the issue with AI first'. It clearly identifies the verb-resource pair (try solve issue) and distinguishes from siblings like assign_expert which assigns experts. However, it does not explicitly differentiate from add_issue or process_issues.

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 implies a workflow by stating 'try to solve with AI first' and the return message suggests assigning an expert if no solution is found. However, it lacks explicit guidance on when to use this tool versus alternatives, such as prerequisites or cases where it should not be used.

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