Genexxis paid agent tools (x402)

guard_check

Classify text an AI agent is about to read for prompt injection: verdict (safe, suspicious, malicious), score 0-100, findings with matched spans; decodes hidden Unicode tags, HTML comments, CSS-hidden text and base64 before scanning. Price: $0.01 USDC per call, paid with x402 on Base.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "guard_check"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	Untrusted text, up to 20,000 characters
`context`	No	web \| email \| document \| tool_output \| user \| unknown

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 24, 2026

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden and does well: it discloses preprocessing behavior (Unicode tags, HTML comments, CSS-hidden text, base64 decoding), the output contract, and the pricing/payment mechanism (x402, USDC on Base). It omits error behavior, latency, and rate limits, 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two dense sentences, purpose front-loaded, each clause earning its place (capability, preprocessing, output, cost). No filler or repetition of the tool name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With only two params and no output schema, the description supplies the return shape itself (verdict values, 0-100 score, findings with spans) and the payment precondition, which is what an agent most needs. Only error/failure semantics are absent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so both `text` and `context` are already documented in the schema. The description adds no meaning beyond that — it never mentions the `context` hint or its allowed values — so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Specific verb (classify) + resource (text for prompt injection) plus the concrete output shape (verdict/score/findings). It is clearly distinguishable from every sibling, none of which do content-security classification of text.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'text an AI agent is about to read' gives a clear triggering context, so an agent knows to call it before ingesting untrusted content. However, no siblings or alternatives are named and no explicit exclusions (e.g. when not to bother) are given.

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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