Analyzes text to determine sentiment polarity and subjectivity, returning results as JSON.
Analyze the sentiment (polarity and subjectivity) of text.
Args: text: The input text to analyze
Returns: JSON string with sentiment analysis results
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the return format (JSON string) and does not disclose read-only nature, side effects, or any limitations. This is minimal for a tool with zero annotation coverage.
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?
The description is short and front-loaded with the purpose, followed by a clear Args and Returns structure. No wasted words or redundant 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 simple single-parameter tool with an output schema, the description covers the essential behavior. It doesn't explain edge cases, but the simplicity makes it sufficient. The mention of polarity and subjectivity clarifies the scope.
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?
The description explains that the text parameter is the input text to analyze, which adds slight meaning beyond the schema's bare type. However, with 0% schema description coverage, more detail (e.g., length limits or encoding) could be provided, though it is adequate for a single simple parameter.
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 clearly states the verb 'analyze' and the resource 'sentiment of text', explicitly specifying polarity and subjectivity. This distinguishes it from siblings like extract_keywords or detect_language, making its purpose unambiguous.
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 guidance is provided on when to use this tool versus alternatives such as analyze_text or summarize_text. The description does not mention any conditions, exclusions, or preferences, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.