Audit TikTok Shop & Amazon affiliate scripts for policy violations via MCP.
Scored across 2 tools
audit_script and generate_appeal serve clearly distinct purposes: one analyzes content for violations, the other creates an appeal response. There is no overlap in function or input, so an agent can easily select the right tool.
Both tool names follow a consistent verb_noun snake_case pattern (audit_script, generate_appeal). The naming is predictable and directly reflects the action and target.
With only two tools, the set feels thin for a compliance-focused server. While each tool is meaningful, the narrow surface likely leaves out useful operations such as rewriting a script or checking other content types.
The server covers proactive auditing and reactive appeals, but audit_script only mentions TikTok Shop and Amazon affiliate scripts, while generate_appeal also mentions YouTube. There is no tool to generate a compliant script from scratch or audit other content formats, leaving notable gaps.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| script | Yes | The full video script text to audit. | |
| product_url | No | 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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It does well by disclosing the detection taxonomy and the return payload (flagged phrases, reasons, safe rewrites, overall risk level), which is meaningful behavioral context. It stops short of stating that the operation is read-only with no side effects or any auth/rate constraints.
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?
Two sentences, front-loaded with the core action before the detection list and the return summary. The categorical list is long but each item is load-bearing since it defines scope; only minor trimming would help.
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?
There is no output schema, so the description must describe returns — and it does, naming the flagged phrases, reasons, rewrites, and risk level. Coverage is solid for a single-purpose analysis tool; what's missing is only peripheral (read-only nature, relationship to generate_appeal).
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%, so both 'script' and 'product_url' are already fully documented in the schema, including the cross-check behavior of product_url. The description adds no parameter-level detail beyond that, so the baseline 3 applies.
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?
States a specific verb (audit), a specific resource (TikTok Shop or Amazon affiliate video script), and enumerates the exact violation categories detected. An agent can distinguish this immediately from the sibling generate_appeal, which is a downstream remediation tool.
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?
Usage is implied by the purpose — you call it when you have an affiliate script to check — but there is no explicit when/when-not guidance, no prerequisites, and no mention of how it relates to generate_appeal (e.g., audit first, then appeal). Nothing is misleading, but nothing routes the agent either.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a ready-to-submit appeal response for a TikTok, Amazon, or YouTube policy violation notice. Stays within TikTok's 800-character appeal limit. Paste the violation notice text you received and specify the platform.
| Name | Required | Description | Default |
|---|---|---|---|
| platform | Yes | The platform that issued the violation: tiktok, amazon, or youtube. | |
| violation_notice | Yes | The full text of the violation notice or strike you received from the platform. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It usefully discloses one constraint – output stays within TikTok's 800-character appeal limit – and frames the result as 'ready-to-submit.' However, it says nothing about whether output is editable, deterministic, or requires human review, nor about any rate limits or auth needs for a generation tool.
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?
Three short sentences, front-loaded with the outcome, then the key constraint, then the input instructions. Every sentence carries information; no padding or repetition of boilerplate.
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?
There is no output schema, so the description should ideally characterize the returned artifact. It gestures at this ('ready-to-submit appeal response,' character-limited) but never says whether the output is plain text, structured, or accompanied by metadata. Adequate but leaves a real gap for a generation tool.
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%, so both parameters are already fully documented, including the platform enum. The description restates the same information ('paste the violation notice text,' 'specify the platform') without adding format, length, or example details beyond the schema.
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 names a specific verb+resource ('Generate a ready-to-submit appeal response') and scopes it to three named platforms, so the agent immediately knows what the tool produces. It does not differentiate from the sibling audit_script, but the two are clearly unrelated tasks, so the gap is minor.
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?
Usage is implied by the instruction 'Paste the violation notice text you received and specify the platform,' which conveys the precondition (you have a violation notice). There is no explicit when-not-to-use guidance, no mention of the alternative audit_script, and no statement of what to do if the notice is from a different platform.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
audit_script1 field changedInput schema / properties / product_urlAdded 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"
+}generate_appealaudit_script
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