snowflake_cortex_analyst_queryAsk a natural-language analytical question using Cortex Analyst semantic models to retrieve SQL-backed answers from Snowflake data without writing SQL.
Ask a natural language analytical question using Cortex Analyst semantic models.
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | ||
| semantic_model_path | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v1.2.0Input schema / additionalPropertiesAdded value: +falseInput schema / properties / question / titleRemoved value: -"Question"Input schema / properties / semantic_model_path / titleRemoved value: -"Semantic Model Path"Input schema / titleRemoved value: -"snowflake_cortex_analyst_queryArguments"Output schema / titleRemoved value: -"snowflake_cortex_analyst_queryDictOutput"v0.1.0Does 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 clear. The description adds that the operation is natural-language and uses semantic models, but does not disclose latency, cost, or authentication requirements.
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 sentence with no filler, front-loading the core purpose. Every word earns its place, and there is no redundancy.
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?
Despite having an output schema and annotations, the description is incomplete for a tool with two parameters and 0% schema coverage. It does not explain the optional semantic_model_path parameter, nor does it give usage context relative to siblings like snowflake_query.
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 must compensate for two undocumented parameters. It hints at the 'question' parameter via 'natural language analytical question' and vaguely references semantic models, but never explains the optional semantic_model_path parameter, its format, or what happens when it is omitted.
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 states a clear verb ('Ask') and resource ('natural language analytical question using Cortex Analyst semantic models'), which distinguishes it from generic SQL query and other Cortex text tools. However, it does not explicitly name alternatives or scope boundaries.
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 when-to-use guidance or alternatives are provided. The description implies it is for analytical questions, but does not say when to choose it over snowflake_query or other Cortex functions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.