sense

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

No annotations are provided, so the description must be fully transparent. It describes the behavior: analyzes goal, matches metadata, returns ranked suggestions. It adds context beyond the schema, such as no AI inference. Minor missing details like no mention of authentication or rate limits, but overall strong.

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 at four sentences, well-structured with purpose, usage context, behavioral explanation, and an example. No extraneous information.

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 has one parameter, no output schema, and no annotations, the description is complete. It explains what it does, how it works, when to use it, and provides an example. All necessary context for an agent to use the tool correctly.

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?

The sole parameter 'goal' is fully described in the schema (100% coverage). The description adds an example but doesn't provide additional constraints or semantics beyond the schema. Baseline of 3 is appropriate.

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 as getting module recommendations based on a goal, with specific verbs ('Get') and resource ('module recommendations'). It distinguishes from sibling 'forage' by noting it's faster for broad exploration, and clarifies it uses keyword matching not AI inference.

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 explicitly says when to use: 'when the user describes what they want to achieve and needs guidance on which modules to study.' It also contrasts with the sibling 'forage' for broad exploration, providing clear guidance on selection.

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