toggle_auto_learn

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that enabling auto-learning causes Tessera to 'automatically extract decisions, preferences, and facts from conversations,' which adds useful context about what the feature does. However, it doesn't mention potential side effects, permissions needed, or rate limits, leaving some behavioral aspects unclear.

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 front-loaded with the core purpose, followed by specific usage instructions. Both sentences earn their place by providing essential information without redundancy, making it highly efficient and well-structured.

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 that there is an output schema (which handles return values), no annotations, and a simple parameter structure, the description is reasonably complete. It covers purpose, usage, and parameter behavior adequately, though additional details on error conditions or system impacts could enhance completeness for a toggle operation.

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 schema description coverage is 0%, so the description must compensate. It effectively explains the parameter semantics: the 'enabled' parameter is optional (call without arguments to check status), and when provided, it toggles the auto-learning status. This adds meaningful context beyond the bare schema, though it could specify what 'null' means more explicitly.

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 with specific verbs ('toggle or check') and resource ('auto-learning status'), and distinguishes it from siblings by specifying its unique function of managing Tessera's auto-learning feature, which none of the listed sibling tools address.

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 provides explicit guidance on when to use the tool: 'Call without arguments to check status' indicates the default behavior, and the presence of an 'enabled' parameter implies it can be used to toggle the status. This clearly differentiates it from alternatives like 'learn' or 'extract_decisions' which perform different functions.

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