snowflake_cortex_embed_text_768

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

No annotations are provided, so the description must carry the burden of behavioral disclosure. It only states the action (generate embeddings) but doesn't disclose whether this is read-only, any side effects, authentication needs, or rate limits. It also doesn't mention the response format or any potential failure modes.

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 sentence, concise and direct, with the key purpose front-loaded. No unnecessary words or repetition of the tool name.

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 an output schema (not shown), so return values are covered. However, the description doesn't provide essential context like input constraints, model options, or limitations. For a 2-parameter tool, more guidance is expected.

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 input schema has 0% description coverage, so the description must compensate. It mentions 'for text' which maps to the text parameter, but gives no information about the model parameter, its default, or options. The description adds only marginal meaning beyond the parameter names.

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 tool generates 768-dimensional dense vector embeddings for text using Snowflake Cortex AI. This distinguishes it from sibling cortex functions like completion or summarization, and from other snowflake tools.

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 like other Cortex functions or other embedding tools. It doesn't mention any use cases or conditions that would make this tool the right choice.

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