Classify text sentiment as positive or negative with a confidence score.
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"text": "<text>"
},
"name": "sentiment"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to classify (max 2000 characters) |
Changes observed during successful MCP inspections.
Input schema / properties / textAdded value: +{
+ "description": "Text to classify (max 2000 characters)",
+ "type": "string"
+}Input schema / requiredAdded value: +[
+ "text"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it does disclose the result shape (positive/negative label plus confidence score), which is valuable given there is no output schema. However, it omits how neutral or mixed text is handled, language support, and whether the 2000-character limit is enforced by truncation or error.
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, front-loaded with the verb and resource, with the return shape appended rather than buried. Nothing is wasted or redundant.
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 one-parameter classifier with no output schema, stating the label set and confidence score covers the essentials. The remaining gap is disambiguation from the duplicate sentiment sibling, which is a routing concern rather than a calling concern.
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?
Single parameter with 100% schema description coverage, including the max-length constraint, so the schema already does the work. The description adds no format or preprocessing guidance for the text argument, matching the baseline for high-coverage schemas.
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?
Names a specific verb and resource (classify text sentiment) and even specifies the output shape: binary positive/negative plus a confidence score. It falls short of a 5 because it never acknowledges the near-identical sibling analyze_sentiment, so an agent cannot tell from the text which of the two to call.
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?
There is no when-to-use guidance at all — no input length constraints beyond the schema, no language scope, and no routing hint versus analyze_sentiment, which appears in the sibling list with an almost identical purpose. The agent must guess which sentiment tool is canonical.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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