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Glama

scan_payload_safety

Scans raw text, prompts, or tool arguments for indirect prompt injections, SSRF IPs, zero-width unicode steganography, and shell injection attacks before they reach LLM context.

Instructions

    [Cost: $0.002 USDC on Base & Solana] Sub-5ms deterministic security shield scanning raw text, prompts, or tool arguments
    for indirect prompt injections, SSRF IP addresses, zero-width unicode steganography, and shell injection attacks.
    Scan inputs before feeding them to LLM context to prevent prompt override or credential exfiltration.

    Args:
        payload: Raw text, user prompt, or JSON tool payload to scan.
        strict_mode: Apply stricter risk thresholds (default: False).
        payment_signature: Optional x402 Base USDC transaction hash or developer mock key.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payloadYes
strict_modeNo
payment_signatureNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.3.0

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses cost, sub-5ms latency, determinism, and the fact that it scans rather than modifies inputs. However, it does not explain what the response looks like, how failures are reported, or the exact role/payment semantics of the payment signature, leaving meaningful behavioral gaps.

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?

The description is tight and front-loaded: cost, latency, behavior, threat scope, and use case appear first, followed by a compact Args block. Every sentence earns its place; there is no filler or repetition.

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

Completeness3/5

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

For a paid, 3-parameter tool with no output schema and no annotations, the description should explain return values and payment behavior more explicitly. The cost and payment_signature are mentioned, but it is unclear what the agent receives back or what happens if payment is not supplied. This is adequate but not fully complete.

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

Parameters5/5

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

Despite 0% schema description coverage, the tool description documents all three parameters with meaningful semantics: payload is the raw text/prompt/JSON to scan, strict_mode adjusts thresholds, and payment_signature is an x402 transaction hash or developer mock key. This adds real value beyond the bare schema types and defaults.

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 names a precise verb+resource: a security shield that scans raw text, prompts, or tool arguments for a specific set of threats (prompt injection, SSRF IPs, zero-width unicode, shell injection). It clearly distinguishes itself from weakly related siblings like check_address_risk or classify_url by stating its exact coverage and use case.

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?

It explicitly tells the agent when to use it: 'Scan inputs before feeding them to LLM context to prevent prompt override or credential exfiltration.' It provides clear context but does not name specific sibling alternatives or exclusion conditions, so it falls just short of full routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.