select_chat_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 mentions presenting models and helping with selection, but fails to detail how this is done (e.g., interactive UI, list display, filtering criteria), what happens after selection (e.g., does it initiate chat automatically?), or any constraints like rate limits or permissions. This leaves significant gaps in understanding the tool's 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?

The description is extremely concise and front-loaded, consisting of a single, clear sentence: 'Present available models and help user select one for chat'. Every word contributes directly to the tool's purpose, with no wasted information or redundancy, making it efficient for quick comprehension.

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 complexity (involving model selection and chat initiation), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., selected model details, chat initiation status), behavioral nuances, or how it integrates with siblings like 'local_llm_chat'. For a tool with one parameter but potential behavioral depth, more context is needed to fully guide an AI agent.

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 input schema has 100% description coverage, with one parameter 'message' documented as 'The message the user wants to send after selecting a model'. The description adds no additional meaning beyond this schema, as it doesn't explain parameter usage or constraints. Given the high schema coverage, a baseline score of 3 is appropriate, as the schema adequately handles parameter semantics without extra description input.

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 purpose: 'Present available models and help user select one for chat'. It specifies the verb ('present' and 'help select') and resource ('models'), making the function understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_local_models' or 'suggest_models', which may have overlapping functionality.

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 minimal guidance on when to use this tool. It implies usage when a user needs to select a model for chat, but offers no explicit context on when to choose this over alternatives like 'list_local_models' or 'suggest_models', nor does it mention prerequisites or exclusions. This lack of comparative guidance reduces its effectiveness for an AI agent.

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