remove_model

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 states the action is 'Remove' (implying destructive mutation) but lacks details on permissions needed, whether removal is reversible, error handling (e.g., if model doesn't exist), or side effects (e.g., impact on default settings). The force parameter hint in the schema suggests some behavioral nuance, but the description doesn't elaborate.

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 a single, direct sentence with zero wasted words. It front-loads the core action and resource, making it highly efficient and easy to parse. Every word earns its place by conveying essential purpose.

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 the tool's destructive nature, lack of annotations, and no output schema, the description is insufficiently complete. It doesn't address critical context like what 'local storage' entails, confirmation requirements, success/failure indicators, or integration with sibling tools (e.g., checking models first with 'list_local_models'). For a mutation tool, this leaves significant gaps.

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 100%, so the schema fully documents both parameters (model_name and force). The description adds no parameter-specific information beyond what's in the schema, such as format examples for model_name or implications of using force. This meets the baseline for high schema coverage.

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 action ('Remove') and target resource ('a model from local storage'), making the purpose immediately understandable. However, it doesn't differentiate from potential sibling tools like 'list_local_models' or 'select_chat_model' beyond the obvious destructive nature, which prevents a perfect score.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a model to exist locally), exclusions (e.g., not for remote models), or relationships with siblings like 'list_local_models' for verification. This leaves the agent with minimal context for decision-making.

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