complete

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

No annotations are provided, so the description carries the full burden. It usefully discloses one behavior — that leaving `temperature` unset delegates sampling to llama-server — but says nothing about resource cost, latency, whether the call is stateful, or how the model is selected. Partial disclosure for a no-annotation tool.

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, front-loaded sentences with no filler; the key differentiator (no chat template) comes first. Minor stray indentation/whitespace in the text is the only blemish.

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, and the tool is conceptually simple. But for a 4-parameter tool with zero annotation and zero schema-description coverage, the description is too thin to fully orient an agent on parameter behavior.

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 0% across 4 parameters, so the description must compensate, yet it only clarifies `temperature`. The meaning of `model`, `prompt`, and `n_predict` (including its 256 default and relationship to output length) is left 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 specific verb+resource ('Continue raw text with the local model') and adds the distinguishing constraint 'with no chat template applied', which implicitly separates it from the `chat` sibling. It never names the sibling, so differentiation is inferred rather than explicit.

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 phrase 'no chat template applied' implies this is the tool for raw/completion-style use as opposed to `chat`, but there is no explicit when-to-use statement, no exclusion, and no named alternative. Usage is left to inference.

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