Classify text into exactly one of your own labels using an LLM. Body: text (max 4000 chars), labels (2-20 strings). Price: $0.005 per call (x402, USDC on Base).
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to classify, max 4000 chars | |
| labels | Yes | Allowed labels, 2-20 |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does substantial work: it discloses per-call pricing ($0.005), the payment rail (x402, USDC on Base), the input ceiling (4000 chars), the label count bounds, and the single-label output cardinality. It omits error/failure behavior, latency, and whether retries re-charge, which keeps it short of a 5.
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?
Three telegraphic sentences, zero filler, purpose front-loaded before the parameter recap and pricing note. Every clause carries information an agent needs.
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?
There is no output schema, so the description correctly compensates by stating the result is exactly one label, and it adds the cost/payment context a caller needs before invoking. Only failure modes and invalid-label handling are unaddressed.
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 100%, so both parameters (text, labels) are already fully documented in the schema. The description's 'max 4000 chars' and '2-20 strings' merely restate those constraints rather than adding syntax, format, or example meaning, so the baseline 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?
States a specific verb (classify), the resource (text), the mechanism (LLM), and a precise output constraint ('exactly one of your own labels'). The sibling set is entirely unrelated domains, so no differentiation is needed and an agent can immediately tell what this does.
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 or when-not-to-use guidance and no alternatives named. 'your own labels' implies the caller supplies the taxonomy, but nothing tells the agent in what situations this tool is appropriate versus the sibling validation tools.
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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