cortex_embed_text_768Generate 768-dimensional vector embeddings for text using Snowflake Cortex AI to support semantic search, clustering, or retrieval tasks.
Generate 768-dimensional dense vector embeddings for text using Snowflake Cortex AI.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| model | No | snowflake-arctic-embed-m |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v2.0.4Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint, so safety and repeatability are covered structurally. The description adds only the fixed output dimensionality; it says nothing about cost, latency, model determinism, or whether the same text always yields the same vector beyond what idempotentHint implies.
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?
A single front-loaded sentence with no filler; the key fact (768-dimensional embeddings) appears immediately. Nothing is redundant with the title.
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?
An output schema exists, so return values need no explanation, and the tool surface is small. However, the unexplained 'model' parameter and total absence of usage context leave an agent unable to decide when this tool is the right choice over cortex_search or cortex_complete.
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 description carries the full burden, yet it never mentions the 'text' input or explains the 'model' parameter (its default snowflake-arctic-embed-m, or what alternative models imply for dimensionality). With two undocumented parameters the description should compensate and does not.
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 ('Generate') plus a precise resource ('768-dimensional dense vector embeddings for text') and names the underlying service (Snowflake Cortex AI). The output dimensionality and modality are specific enough to separate it from siblings like cortex_complete, cortex_summarize, and cortex_search without opening any schema.
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 never says when to use embeddings versus the other Cortex tools (e.g., search for retrieval, complete for generation), nor any prerequisites such as an active warehouse or Cortex entitlement. It describes what the tool is, not when to reach for it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.