snowflake_cortex_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 of behavioral disclosure. It only states what the tool does without mentioning side effects, cost implications, permission requirements, or any special behavior (e.g., whether it reads from warehouse context, rate limits). For a generative tool, this is a significant gap.

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 a single concise sentence, but it under-specifies rather than being efficiently informative. It does not front-load the most critical behavioral or usage data. It is short but not necessarily 'good conciseness' because it omits essential guidance.

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 there is an output schema, the return format need not be described, but the tool has 4 parameters and is one of many analogous Cortex tools. The description is too sparse: it lacks any note on the difference from other Cortex tools, parameter nuances (e.g., temperature range), or typical usage scenarios. An agent is left without enough context to use it confidently.

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 description provides zero information about parameters. The schema has 4 parameters (model, prompt, max_tokens, temperature) but schema coverage is 0%, and the description does not compensate. It does not even mention that 'prompt' is required or that 'model' has a default. The agent must infer everything from the schema.

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 action ('Run LLM completion') and the resource (Snowflake Cortex AI), with concrete model examples. It distinguishes itself from specialized cortex tools (like summarize, sentiment) by indicating it is a general completion tool, though it does not explicitly name a sibling as an alternative.

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 use this tool versus other Cortex AI tools or other Snowflake tools. It does not mention that it is for general text generation and not for summarization, translation, or other specialized tasks. The examples give a hint but no explicit usage context.

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