cortex_analyst_queryAsk natural-language analytical questions against Snowflake data with Cortex Analyst semantic models, optionally specifying a semantic model path.
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.
v2.0.4Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds that queries run over semantic models via natural language, but does not disclose latency, model resolution, or what happens when semantic_model_path is absent.
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. It is efficient, though the brevity comes at the cost of the missing detail noted elsewhere rather than being genuinely complete.
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 needn't be explained, but with 0% parameter coverage, no usage guidance, and an undefined optional parameter, the description is too thin for an agent to invoke this correctly in one shot.
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. It only loosely implies the 'question' parameter and says nothing about semantic_model_path, its optional default, format, or what the model path should point to.
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 specific verb ('Ask') and resource ('natural language analytical question') and ties it to Cortex Analyst semantic models. This distinguishes it from raw-SQL siblings like queries_query, though it never explicitly names an alternative such as cortex_complete or queries_query.
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 phrase 'natural language analytical question' implies the context but is not framed as explicit guidance. There is no when-to-use vs when-not-to-use, no mention of alternatives like queries_query for SQL, and no prerequisites such as needing a semantic model.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.