Snowflake Cortex Complete

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds no behavioral context such as latency, cost, rate limits, or output format, which are relevant for an LLM completion tool that may involve external model calls.

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, front-loaded sentence with no unnecessary words. It efficiently conveys the tool's action and provides useful model examples without padding.

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?

With four parameters and 0% schema coverage, the description is insufficient for an agent to call the tool correctly. It omits explanations for required and optional parameters, does not mention output format or how to handle responses, and lacks any usage context despite the tool's complexity.

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%, so the schema provides no clarifications for any parameter. The description only lists model name examples but does not explain the roles of prompt, max_tokens, temperature, or their defaults. It fails to compensate for the complete lack of schema documentation.

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 (Run LLM completion) and clearly identifies the resource (Snowflake Cortex AI). The parenthetical examples of model names ('llama3.3-70b', 'mistral-large2', 'snowflake-arctic') distinguish it from siblings like snowflake_cortex_summarize or snowflake_cortex_sentiment, which are task-specific rather than general completion.

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 use this tool versus alternatives. It does not explain when to choose this over snowflake_cortex_summarize, snowflake_cortex_sentiment, snowflake_cortex_translate, or snowflake_cortex_extract_answer, nor does it mention any prerequisites or constraints.

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