Audit TikTok Shop & Amazon affiliate scripts for policy violations via MCP.
Average 4.2/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of ambiguity or overlap. The tool's purpose is clearly defined and distinct.
A single tool name follows a clear verb_noun pattern (audit_script), which is consistent. There is not enough surface area to fully evaluate consistency, but the naming is clear and predictable.
One tool is minimal but appropriate for a highly specialized server focused on a single audit task. It feels slightly thin, but the scope is narrow enough that the count is reasonable.
The tool fully covers the stated purpose of auditing scripts for policy violations. The domain is narrow, so additional tools may not be necessary, though a helper to re-audit multiple scripts or manage audit history could be a minor gap.
audit_scriptAudit script for policy violationsAAudit a TikTok Shop or Amazon affiliate video script for policy violations (medical claims, guarantees, false certifications, unproven efficacy). Returns flagged phrases, reasons, safe rewrites, and an overall risk level.
| Name | Required | Description | Default |
|---|---|---|---|
| script | Yes | The full video script text to audit. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnlyHint and idempotentHint, so the description need not restate safety. It adds meaningful behavioral detail by describing what the tool returns: flagged phrases, reasons, safe rewrites, and an overall risk level. This goes beyond the annotations and helps set expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler. The first sentence front-loads the action and target, and the second sentence compactly lists the return value components. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool with no output schema, the description covers both what to provide and what to expect in return. It could specify risk-level values or the full policy taxonomy, but nothing essential for an agent to invoke the tool correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 the script parameter as 'The full video script text to audit.' The description adds context about the script origin but provides no additional parameter-level guidance beyond what the schema offers, 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Audit'), a specific resource ('TikTok Shop or Amazon affiliate video script'), and the purpose ('policy violations'). It lists concrete violation categories and output types, making the tool's function unmistakable even without siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly defines when to use the tool: when auditing a TikTok Shop or Amazon affiliate video script for policy violations. There are no sibling tools or alternatives to contrast with, and the intended context is clear, though it could be slightly more explicit about exclusions.
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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