switch_model

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

Even without annotations, the description discloses key behavioral traits: persistence to a specific config file path, that the setting applies to all future operations, and a detailed return JSON with status, previous/current model, and config file location. This fully compensates for missing annotations.

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 well-structured with clear sections (Purpose, Parameters, Returns, Notes). Each section adds useful information without redundancy, and the main purpose is stated upfront.

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 simple one-parameter configuration tool, the description covers all relevant context: side effects (persistence), config file location, return format, and a pointer to a related tool for verification. It is fully sufficient for an agent to use it correctly.

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 already provides a description ('Model ID') with 100% coverage, and the description adds examples and clarifies the parameter is the model to switch to. This goes slightly beyond the schema's baseline.

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 identifies the tool's function: switching and persisting the default AI model for search and fetch operations. It uses a specific verb ('switches') and distinguishes itself from sibling tools like web_search and get_config_info.

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 lists concrete use cases (changing the model, testing models, persisting preferences) and references get_config_info for verifying available models. However, it does not explicitly state when not to use the tool or compare against all siblings.

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