recommend_components

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 mentions the analysis process but does not disclose behavioral traits such as idempotency, resource consumption, or error handling. The description lacks transparency on behavior beyond the basic operation.

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 concise and well-structured: purpose, mechanism, examples, then parameter docs. It front-loads the key information. Minor redundancy in explaining 'analyses' could be trimmed, but overall efficient.

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 presence of an output schema, the description handles inputs well but omits contextual details like edge cases, no-match handling, or differentiation from similar tools. It is adequate but not fully complete for complex usage.

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?

With 0% schema coverage, the description fully documents all three parameters: description with illustrative examples, max_results with default and max, and include_pro with its toggle meaning. This adds substantial value beyond 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 recommends components for a use case or design goal by analyzing descriptions. It differentiates from simple search tools but not explicitly from similar tools like 'match_components_to_project'. Thus, it's clear but lacks full sibling differentiation.

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 examples that imply usage for natural language queries, but it does not explicitly state when to use this tool versus alternatives like 'search_components' or 'filter_by_scores'. Usage guidance is implicit and not robust.

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