normahub_prompt_model

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

With no annotations, the description carries the full behavioral burden and discloses nothing about auth requirements, rate limits, latency, cost implications, or whether output is streamed or returned whole. A model-generation call that may incur balance charges is exactly the case where cost/quota context matters, and none is present.

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?

One sentence, front-loaded with the action and resource, with zero filler. It is arguably too terse rather than too long, but nothing in it is wasted.

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 six-parameter LLM invocation with no annotations and no output schema, the description should at least hint at what comes back and how to choose prompt vs messages. The schema covers inputs well, so it is adequate but with clear gaps.

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 all six parameters (including the prompt/messages exclusivity and the model examples) are already documented inline. The description adds no parameter-level meaning beyond the schema, which is the expected baseline here.

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 and resource ('Send a prompt to LLM models via NormaHub API') and names concrete model families, which is enough to separate it from the media-generation siblings. It stops short of saying whether this is a one-shot completion or a chat orchestration endpoint, but the core purpose is unambiguous.

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?

No guidance on when to pick this over normahub_list_models for discovery, or how prompt vs messages mode should be chosen. The agent must infer all routing decisions from the schema alone.

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