recommend_libraries

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

With no annotations, the description carries the full burden. It explains the tool recommends libraries and generates/merges sampling prompts, but does not disclose side effects, permissions, idempotency, or rate limits. The mention of 'saves you from guessing' hints at behavior but lacks depth.

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 well-structured with a one-line summary, then a 'WHEN TO USE' section with bullet points. It is concise yet informative, with no redundant or unnecessary sentences.

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 complexity (12 parameters, many siblings), the description does a good job covering the main purpose and usage context. It explains the tool's role in the workflow and mentions external triggers. An output schema exists, so return values are covered. Minor gap: no mention of other use cases or limitations.

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 100%, so baseline is 3. The description does not add significant meaning to individual parameters beyond what's in the schema. The 'WHEN TO USE' section is about usage, not parameter semantics.

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: 'Recommend libraries for a scenario or generate/merge sampling prompts.' It also tells the agent it helps discover libraries, which distinguishes it from sibling tools like analyze_scenario (which analyzes but does not recommend).

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 explicit when-to-use guidance: 'IMMEDIATELY after analyze_scenario, before execute_step' and 'When you encounter "No keyword with name" errors'. This gives clear context, though it lacks explicit when-not-to-use alternatives; but the positive guidance is strong enough.

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