set_learning_configuration

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

With no annotations, the description carries the full burden. It discloses that the program is never modified, that the configuration is stored at session level, and that unrecognized requests are reported back. These are key behavioral traits beyond the bare action.

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 with no filler. The purpose is front-loaded, and the examples are concise and illustrative. Every sentence adds value.

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 description covers the main action, storage behavior, and error handling for unrecognized requests. However, it does not clarify the relationship between `request` and `mode` when both are provided, nor the role of `request_id`. For a 5-parameter tool with no output schema, these gaps leave some ambiguity.

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 0%, so the description must compensate. It thoroughly explains `request` and lists recognized `mode` values, but leaves `overrides` and `request_id` unexplained. It hints at 'Policy Overrides' but does not describe the parameter usage, so compensation is partial.

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 states a specific verb ('apply'), a resource ('learning mode'), and scopes it to a learner's request. It distinguishes itself from the sibling get_learning_configuration by clearly being the write counterpart, and it enumerates recognized modes.

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?

It gives clear guidance on how to use the tool (pass the learner's own words in `request`) with concrete examples, and explains the fallback for unrecognized requests. It does not explicitly mention the alternative get_learning_configuration, but the intent is obvious from context.

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