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prompt guard

prompt_guard

Scores prompt-injection / jailbreak risk for LLM inputs. Agent builders use this to sanitize user inputs before hitting their LLM. [price: $0.01/call USDC via x402]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesInput text

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It adds useful context such as pricing and the guard-oriented use case, but it does not disclose the return format, score interpretation, or whether high risk is indicated by a threshold. This is a meaningful gap for an agent deciding how to act on the output.

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 sentences with no filler. The core purpose is front-loaded, the use case is stated, and the pricing note adds actionable operational information without bloating the description.

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?

For a single-parameter, no-output-schema guard tool, the description covers what it does, when to use it, and the cost. The main missing piece is the nature of the returned score, but overall it is adequately complete for the tool's simplicity.

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% with a clear 'Input text' description, so the baseline is 3. The description adds slight extra meaning by framing the text as 'LLM inputs' in the context of prompt-injection risk, but it does not go beyond that.

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?

The description uses a specific verb ('Scores') and resource ('prompt-injection / jailbreak risk for LLM inputs'), making the tool's function immediately clear. It also distinguishes itself from related guards like pii_guard and json_guard by focusing specifically on injection/jailbreak risk.

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?

Explicitly identifies the target use case: agent builders sanitizing user inputs before calling an LLM. It does not state when not to use it or name alternatives, but the context provided is sufficient for a simple one-parameter tool.

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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