preload_model

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 tool sends an empty chat request with a keep_alive parameter, which is key behavioral information. It does not cover all edge cases (e.g., error if model not installed), but provides sufficient behavioral context for typical use.

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, front-loaded with the main purpose, no superfluous words. Every sentence adds value: first states action, second explains mechanism.

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 no output schema and two well-described parameters, the description is complete. It explains the tool's purpose, mechanism (empty chat request, keep_alive), and a prerequisite (model must be installed). No missing information for expected agent use.

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 100%, so baseline is 3. The description adds context about 'warm inference' and 'session' but does not provide additional parameter details beyond what the schema already documents (e.g., model must be installed, keep_alive examples).

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 action: 'Preload a model into VRAM for warm inference.' It uses a specific verb ('preload') and resource ('model'), and distinguishes it from siblings like 'pull_model' (install) and 'list_loaded_models' (list).

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 implies usage for warm inference and session persistence, but does not explicitly state when to use this tool versus alternatives (e.g., 'offload_work'). No exclusions or when-not-to-use guidance is provided.

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