chat

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

No annotations are provided, so the description carries the full disclosure burden. It notes that the prompt goes through the chat template and that the reply is returned, plus one useful default about temperature, but says nothing about model-loading requirements, failure modes, timeouts, or whether an unloaded model is auto-loaded.

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 short sentences, front-loaded with the core action and followed by the one non-obvious default. No filler, though the awkward indented line break slightly hurts readability.

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?

An output schema exists, so return values need not be described, but for a 5-parameter generation tool with zero annotation and zero schema-description coverage, the description is too thin. Model selection, system-prompt semantics, and max_tokens limits are all left to inference.

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 0% across 5 parameters, so the description must compensate and largely does not. It clarifies temperature's default-null behavior relative to llama-server sampling, but leaves model, prompt, system, and max_tokens entirely unexplained.

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?

States a concrete verb (send a prompt), the resource (the local model via its chat template), and the outcome (return the reply). An agent understands the operation, but the sibling 'complete' is not mentioned, so the boundary between chat-template generation and plain completion is left implicit.

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?

There is no guidance on when to prefer this over 'complete', 'tokenize', or 'models', nor any prerequisites (e.g., whether a model must be loaded first). The only conditional note is about the temperature parameter, which is a parameter hint rather than usage context.

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