llm_predict

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals only that the LLM is 'local', implying offline execution and privacy, but says nothing about response format, nondeterminism, latency, errors, or side effects. This is insufficient for a tool whose output will be consumed by the agent.

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 one short sentence with no filler, tautology, or duplication. It front-loads the action and resource. This efficiency is good, though it comes at the expense of necessary detail, so it does not reach a perfect 5.

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?

The tool has no output schema and no annotations, so the description must explain what the agent can expect from a call. It fails to mention that the tool returns a text completion, how 'max_tokens' affects behavior, or any example invocation. Even for a simple two-parameter tool, an agent lacks enough information to confidently interpret the response.

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 already fully documents both 'prompt' and 'max_tokens'. The description does not add semantic value beyond restating 'text input', which maps directly to 'prompt'. The baseline of 3 applies because the schema carries the parameter information.

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 identifies a specific resource ('local LLM') and an action ('process text input'), which tells an agent the tool routes text to a language model. The verb 'process' is generic and does not explicitly say 'generate' or 'predict', but the tool name 'llm_predict' disambiguates the intent. It does not mention why this differs from sibling 'echo', so it is not fully differentiated.

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 choose this tool over its sibling 'echo' or any alternative. There are no stated conditions, exclusions, or example use cases. An agent would have to infer from the name alone that this is for LLM inference, but nothing in the text says 'use this when you need model-generated text'.

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