Analyze text sentiment to classify as positive, negative, or neutral. Each call costs $0.01 USDC, enabling pay-per-call sentiment analysis for AI agents.
Sentiment analysis: positive / negative / neutral ($0.01 USDC/call).
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to analyze |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the per-call cost ($0.01 USDC/call), which is a behavioral trait not present in annotations or schema. It also implies the output is one of three sentiment labels. However, with no annotations, the description carries the full burden and does not cover auth, rate limits, or side effects, leaving some gaps.
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 a single sentence that includes the core task, output categories, and pricing. Every word is functional and front-loaded, with no filler or redundancy.
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 tool with one parameter and no output schema, the description is largely complete: it states the task, the expected output categories, and the cost. However, it does not specify the exact response format (e.g., string label vs. confidence score) and lacks usage context relative to siblings, though the simplicity keeps these gaps minor.
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 coverage is 100%; the 'text' parameter is already documented as 'Text to analyze'. The description adds no further parameter-level detail (e.g., length limits, language support), so the baseline score of 3 applies.
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 tool performs sentiment analysis and enumerates the output categories (positive, negative, neutral). It is specific and distinguishable from siblings by name and domain, but it does not explicitly contrast with any alternative tool.
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 like x402_summarize or x402_translate. The description only states what the tool does, with no context for selection or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.