get_recommendations

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

Annotations already mark the tool as readOnly and idempotent. The description adds that no prior indexing is required and explains the enrich flag, providing useful behavioral context beyond annotations.

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?

Three sentences, front-loaded with the core purpose, followed by return type and optional parameter guidance. No redundant information, every sentence adds value.

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 description adequately explains the tool's output (actionable suggestions with scores) and key parameter behavior. Given the complexity (4 params, no output schema) and rich annotations, it covers critical aspects without gaps.

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 100%, so the baseline is 3. The description reinforces schema info (e.g., enrich for LLM descriptions, max_items default/max) but does not add new parameter semantics beyond what's in the schema.

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 it generates prioritized refactoring recommendations from architecture health analysis, with explicit mention of effort/impact scoring. It distinguishes from siblings like get_architecture_health and get_design_smells by focusing on actionable suggestions.

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 gives a clear context (architecture health analysis) and specific guidance for the enrich parameter. It implies use when recommendations are needed, but lacks explicit comparisons to sibling tools or conditions when not to use.

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