snowflake_cortex_sentimentAnalyze text sentiment to gauge opinions and emotions, returning a score from -1.0 (negative) to 1.0 (positive).
Analyze sentiment of text returning score from -1.0 (most negative) to 1.0 (most positive).
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v1.2.0Input schema / additionalPropertiesAdded value: +falseInput schema / properties / text / titleRemoved value: -"Text"Input schema / titleRemoved value: -"snowflake_cortex_sentimentArguments"Output schema / titleRemoved value: -"snowflake_cortex_sentimentDictOutput"v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered structurally. The description adds genuinely useful behavioral detail beyond the annotations: the output is a score on a -1.0 to 1.0 scale. It does not mention cost, latency, language support, or text length limits.
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 zero filler; the operation comes first and the output range follows. Nothing needs trimming.
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 structure need not be explained, and a one-parameter read-only tool is intrinsically simple. The description is nearly sufficient; only input constraints (length, language) are missing to be fully 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% and there is one parameter, so the description must carry the semantics. 'sentiment of text' implicitly names the input, but adds no detail on format, size limits, language coverage, or whether it accepts one string or a batch. Barely compensates for the coverage gap.
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 (analyze sentiment) and resource (text), and even specifies the numeric output range, so an agent knows exactly what operation it performs. It does not explicitly differentiate itself from siblings like snowflake_cortex_complete or snowflake_cortex_summarize, but the purpose is unambiguous on its own.
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?
Usage is only implied: an agent can infer this is the tool for sentiment scoring of a piece of text. There is no explicit when-to-use or when-not-to-use guidance, and no mention of alternatives in the Cortex family (summarize, translate, complete) for cases where the goal is not sentiment.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.