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Glama

BanProof AI

by io.banproof

Audit script for policy violations

audit_script
Read-onlyIdempotent

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

TableJSON 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. Dates show when Glama detected each change.

  1. 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"
+}
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so safety is covered. The description adds genuinely useful behavioral context by listing exactly what the audit detects and what it returns (flagged phrases, reasons, safe rewrites, risk level), going beyond the annotations.

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 compact and front-loaded: it states the action first, then enumerates detection categories and return values in a structured way. Every sentence earns its place, with no filler or repetition of schema fields.

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

Completeness5/5

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

Even though there is no output schema, the description names all return components and detection categories. The schema covers both parameters, and the annotations cover safety and idempotence, so an agent has enough information to call the tool correctly without additional documentation.

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 description coverage is 100%, and the schema already documents both script and product_url in detail, including the cross-checking behavior for product_url. The description adds no additional parameter-level information, so the baseline of 3 is appropriate.

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 ('Audit'), a clear resource ('TikTok Shop or Amazon affiliate video script'), and enumerates concrete detection categories. It is clearly distinct from the sibling generate_appeal, since auditing for violations is a different operation from generating an appeal.

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 description clearly implies when to use the tool: when a script needs a policy-compliance check. However, it never mentions the sibling generate_appeal or provides when/when-not guidance, so an agent must infer the selection decision rather than being explicitly routed.

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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TDQS

A4.2/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: one audits existing scripts for policy violations, while the other generates appeals for received violation notices. There is no meaningful overlap or risk of an agent selecting the wrong tool.

Naming Consistency5/5

Both tool names follow the same verb_noun snake_case pattern: audit_script and generate_appeal. The naming is predictable and consistent.

Tool Count3/5

Two tools feels thin for a general policy-compliance server, though each tool covers a distinct, useful workflow. The count is borderline but not unreasonable for a narrowly scoped niche.

Completeness4/5

The pair covers the main lifecycle well: prevent violations via audit_script and respond to violations via generate_appeal. Minor gaps exist—such as policy lookup or rewrite-only functionality—but audit_script already includes safe rewrites, so the core workflow is not blocked.

Resources