cortex_sentimentAnalyze text sentiment and return a score from -1.0 to 1.0, helping AI agents gauge positive, neutral, or negative tone.
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.
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 the output polarity range (-1.0 to 1.0), which is useful behavioral context, but says nothing about language support, input length limits, model latency/cost, or handling of mixed/neutral text.
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?
One sentence, zero filler, with the operation and the return-value scale both front-loaded. Nothing could be removed without losing information.
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?
For a single-parameter, read-only tool with an output schema that already documents the return shape, this is nearly sufficient. The remaining gap is input-side detail (length limits, language coverage), which the description does not address.
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% for the sole required 'text' parameter, so the description carries the burden but only names the input implicitly. It adds no constraints such as maximum length, supported languages, or whether multiple strings are accepted, leaving the parameter under-documented.
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 and resource ('Analyze sentiment of text') plus the output scale, so the agent immediately knows this is a sentiment classifier rather than a generator or search. It never explicitly positions itself against the other cortex_* text tools, but the purpose is inherently distinct from cortex_complete/summarize/translate.
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 is given. The description does not say when to prefer this over cortex_complete (which could also be prompted to judge sentiment) or whether it handles a single text or a batch. The agent must infer applicability from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.