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agent-guard-scan

Auto-created from research: Escaneo pre-ejecución de entradas/salidas de agentes IA para detectar prompt injection, filtrado de datos sensibles y cumplimiento no autorizado de instrucciones. [x402: 0.02 USDC on Base, pay-per-use]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesentrada

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.2/5.0
Behavior3/5

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

No annotations, so the description carries the full burden. It discloses a real operational fact – x402 pay-per-use at 0.02 USDC on Base – which tells the agent a payment/authorization step is involved. But it never says what the scan does on detection (block, flag, return a verdict) or whether it mutates the input, so the core behavioral contract is only half covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

It is a single compact sentence with a pricing tag, but the leading 'Auto-created from research:' provenance marker is noise that consumes attention without helping an agent select or call the tool, and the main content is a dense run-on of three detections.

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

Completeness2/5

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

With no annotations and no output schema, the description is the only source for behavioral and return information, yet it says nothing about what the scan returns (a verdict, a risk score, flags) or how a caller should act on the result. For a pre-execution gate where the response shape is the whole point, this is a significant omission.

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?

Single parameter at 100% schema description coverage, so the schema already documents it and the baseline is 3. The description adds no syntax, format, or size guidance for the 'input' string, so it does not improve on what the schema provides.

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

Purpose4/5

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

States a specific verb and resource: pre-execution scanning of AI agent inputs/outputs, enumerating three targets (prompt injection, sensitive data leakage, unauthorized instruction compliance). That is concrete enough for an agent to understand the operation. However, it never distinguishes itself from the many near-identical security siblings (agent-injection-scanner, prompt-injection-scanner, injection-guard, agent-injection-firewall), which is the main gap.

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

Usage Guidelines3/5

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

The word 'pre-ejecución' implicitly positions the tool in the workflow (run it before executing agent I/O), which is useful timing guidance. Beyond that there is no when-to-use/when-not, no prerequisites, and no mention of the overlapping sibling scanners an agent should compare against.

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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