Skip to main content
Glama
Sign In

BanProof AI

by io.banproof
OverviewDiscussionsAdmin

audit_script

Audit a TikTok Shop or Amazon affiliate video script for policy violations. Detects: medical claims, guarantees, false certifications, unproven efficacy, urgency/scarcity language, fake social proof, income claims, and missing FTC disclosures (#ad/#sponsored). Returns flagged phrases, reasons, safe rewrites, and an overall risk level.

Input Schema

NameRequiredDescriptionDefault
scriptYesThe full video script text to audit.
product_urlNoOptional: URL of the product page being promoted. When provided, the script is cross-checked against the actual product claims — overclaims are flagged as additional violations.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • removedInput schema / additionalProperties
      Removed value: -false
  2. Changed1 schema field changed
    • addedInput schema / properties / product_url
      Added value: +{
+  "description": "Optional: URL of the product page being promoted. When provided, the script is cross-checked against the actual product claims — overclaims are flagged as additional violations.",
+  "format": "uri",
+  "type": "string"
+}
  3. First observed

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the detection categories, the return payload (flagged phrases, reasons, safe rewrites, overall risk level), and that a product URL triggers cross-checking of overclaims. It does not explicitly state that the operation is read-only and non-persistent, nor anything about latency or script length 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.

Conciseness4/5

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

Two sentences, front-loaded with the core verb and resource, followed by a compact violation taxonomy and the return shape. The eight-item detection list is long but each entry is a distinct, actionable category, so it earns its space; a slightly tighter grouping would push it higher.

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?

No output schema exists, and the description compensates by naming the return fields and the risk-level summary. For a two-parameter, single-shot analysis tool this covers what an agent needs to call it correctly; only edge details (input size limits, whether results are persisted) 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% and both parameters are documented in-schema, including the conditional behavior of product_url. The description adds no parameter-level detail beyond what the schema already provides, so the baseline of 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?

States a specific verb (audit) and resource (affiliate video script) with named platforms (TikTok Shop, Amazon) and an enumerated set of violation classes. This is clearly distinct from the only sibling, generate_appeal, which is a downstream remediation action rather than a check.

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 context of use is strongly implied — you audit a script to catch policy violations before publishing — and the enumerated detection categories hint at when it is relevant. However, there is no explicit 'use this when' statement, no mention of prerequisites or ordering relative to generate_appeal, and no stated exclusions (e.g., non-affiliate content).

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

All BanProof AI toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources