get_pricing
PRO pricing & payment info: 35 probes / 17 attack classes / 0-100 score / CI gate, 3 USDT (ERC-20 or Lightning).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
PRO pricing & payment info: 35 probes / 17 attack classes / 0-100 score / CI gate, 3 USDT (ERC-20 or Lightning).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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. It discloses the informative nature and the payment options, implying this is a read-only lookup. It does not explicitly state that the call has no side effects, what the response format is, or whether 3 USDT is a displayed price or an actual charge.
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 extremely compact: a label followed by a dense, slash-separated list of concrete values. It front-loads the core purpose and every element—35 probes, 17 attack classes, 0-100 score, CI gate, 3 USDT, ERC-20, Lightning—carries meaningful information without filler.
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 no-argument pricing getter, the description provides enough content to let an agent answer the most likely pricing and payment questions. It could be more complete by noting whether 3 USDT is one-time or recurring and explicitly routing actual purchases to get_purchase_flow, but these are helpful additions rather than essential blockers.
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 input schema has zero parameters, so there is nothing for the description to document beyond what the schema already makes obvious. The pricing details are orthogonal to parameters整理, and the baseline for a zero-parameter tool is met.
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 identifies the resource as 'PRO pricing & payment info' and enumerates the exact contents an agent can expect: probes, attack classes, score range, CI gate, and the 3 USDT price. It is unambiguous, though it lacks an explicit verb and does not contrast itself with get_purchase_flow, which also touches payment-related information.
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 use case is implied by the phrase 'PRO pricing & payment info': an agent would call this when the user asks about cost or payment methods. However, the description gives no explicit guidance about when not to use it or how it relates to get_purchase_flow, run_free_scan, or get_scan_result.
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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