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

No annotations are provided, so the description carries the full burden. It discloses that the tool mutates state transition priors and that learning is online/incremental, but it does not discuss side effects, persistence, reversibility, or failure behavior.

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?

A single compact sentence that front-loads the core action and data source with no filler or redundant wording.

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?

For a one-parameter update tool with an output schema, the description is minimally adequate. The main gaps are the structure of the transitions objects and explicit guidance on when to use this tool versus related cognitive learning tools.

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 0% and the only parameter is an open-ended array of objects. The description adds meaning by indicating transitions should be empirical execution traces, but it does not specify the expected object shape or required fields.

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 ('update'), a concrete resource ('state transition priors'), and a source ('empirical execution traces'). It clearly distinguishes the tool from sibling learning tools like learn_from_mistake by focusing on world-model priors rather than mistakes.

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 phrase 'online world model learning' and 'from empirical execution traces' implies when this tool should be used, but it does not explicitly state when to prefer it over alternatives or mention any prerequisites or exclusions.

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