load_model

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

With no annotations, the description carries full burden. It states the model is 'kept resident in memory,' which is a key behavioral detail. It also mentions the return type (calculator_id). No annotation contradiction exists.

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 wasted words. Front-loaded with the core action, then adds parameter context and return value usage. Highly efficient.

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?

Covers purpose, task options, memory residency, and return value linking to downstream tools. Missing explanation for model and device parameters, but given the tool's simplicity, it is fairly complete.

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 description explains the 'task' parameter by listing valid domains, but does not explain 'model' or 'device' beyond defaults in the schema. Schema coverage is 0%, so the description partially compensates but leaves gaps.

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 ('Load') and resource ('FAIRChem model as an ASE calculator'). It distinguishes from sibling tools like list_models and start_relaxation by focusing on the loading step and noting the output is a calculator_id for simulation tools.

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 tells users to pass the returned calculator_id to start_relaxation/start_md, but does not explicitly exclude alternative uses or mention when not to use this tool. It provides clear context for its role in the workflow.

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