get_learned_weights

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

With no annotations provided, the description carries the full burden. It explicitly states this is a 'View' operation, implying a non-destructive read, and describes the content it returns (tool selections and keywords). This gives enough transparency for an agent to understand the tool's behavior and side-effect profile.

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 two sentences, front-loaded with the primary action, and every clause adds value. No redundant information or fluff.

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 tool is simple with two optional parameters and an output schema. The description explains the purpose and what is displayed, which is sufficient for an agent to know when and how to call it. Could mention that it is read-only explicitly, but the 'View' wording covers that.

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 the schema already documents both parameters (top_n and tool_filter) with clear descriptions. The tool description does not add extra meaning to the parameters, but it doesn't need to because the schema is sufficient. Baseline 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 uses a specific verb ('View') and specifies the resource ('learned weights from the search system'). It further explains what the tool shows (which tools selected, for which keywords), clearly distinguishing it from the sibling reset_learned_weights and other actions.

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 states that this tool is for viewing learned weights and explaining the system's learned behavior, which gives clear context for when to use it. It does not explicitly exclude alternatives, but the read-only nature is evident and no other sibling tool would serve this purpose.

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