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

The description indicates this is a read operation (fetch) and specifies ordering ('newest first'). However, without annotations, it omits other behavioral aspects such as whether authentication is required, rate limits, or fallback behavior for empty history. The bar is higher due to no 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?

Two sentences: one for function, one for use case. Every word adds value with no redundancy or irrelevant detail.

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 output schema exists, the description does not need to detail return format. It covers purpose, content, ordering, and use cases. The two parameters are fully documented in the schema, and the description completes the picture.

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% with clear descriptions for 'limit' and 'offset'. The description adds no parameter-specific details beyond the schema and a note on ordering. Per guidelines, high coverage allows a baseline of 3.

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 uses a specific verb ('Fetch'), names the resource ('past recommendation calls'), and details the content ('problems submitted and the model codes that were returned'). This clearly distinguishes it from siblings like 'recommend_models' and 'list_all_models'.

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 states two use cases: 'what did we explore last time?' and avoiding re-recommending the same models. While it does not list when not to use or alternative tools, the context is clear and actionable.

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