gen_lorem
Paid $0.10 (x402, USDC Base): lorem ipsum generator, 1-20 randomized paragraphs. Args: p (int, default 3).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| p | No |
Paid $0.10 (x402, USDC Base): lorem ipsum generator, 1-20 randomized paragraphs. Args: p (int, default 3).
| Name | Required | Description | Default |
|---|---|---|---|
| p | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral burden. It usefully discloses that the tool is paid, the price, currency/network, and that output is randomized within a range, but it does not explicitly describe return format, payment mechanics per call, or side-effect profile.
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 compact single sentence with no filler and front-loads the paid constraint before the function and arguments. It is slightly terse and uses shorthand, but every part contributes useful 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 one-parameter utility, the description covers cost, output type, range, and the default argument. However, it leaves the exact return format and payment workflow implicit, which matters because there is no output schema or annotation to fill those gaps.
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 0%, so the description must compensate. It documents the single argument as p, an integer with default 3, and the surrounding text implies p controls the number of randomized paragraphs. This adds meaningful default-value and semantic context beyond the bare schema constraints.
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 names a specific resource and function: a lorem ipsum generator producing randomized paragraphs. It also states the output range, 1-20 paragraphs, which clearly distinguishes it from generic siblings such as gen_random or gen_password.
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?
The description does not say when to use this tool versus alternatives, nor does it give exclusions or prerequisites beyond noting the paid cost. Usage is only inferable from the tool name and output type.
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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