A
licenseA
qualityB
maintenanceChecks AI-generated social media and ad content against current policies of 8 major platforms, flagging risky phrases and providing compliant rewrites to prevent account restrictions.
1
207
MIT
BanProof AI
Server Details
Audit creator scripts for TikTok Shop and Amazon policy violations. Returns flags and safe rewrites.
Ownership verified
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- NimishRangani/banproof-mcp
- GitHub Stars
- 0
- Server Listing
- banproof-mcp
Available Tools
2 toolsaudit_scriptAudit script for policy violationsARead-onlyIdempotentInspect
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.
ParametersJSON Schema
| 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. |
TDQS
A4.1/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description does not need to restate safety. It adds useful behavioral detail by listing what it detects and what it returns. Minor omissions like advisory nature or non-exhaustiveness are not critical given the annotation coverage.
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 efficient and well-structured: it opens with the core action, then lists detection categories, then states the output. Every sentence carries useful information and there is no filler or repetition of schema/annotation data.
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?
There is no output schema, but the description covers the key return values (flagged phrases, reasons, safe rewrites, risk level). Combined with 100% parameter coverage and read-only/idempotent annotations, the agent has enough information to invoke the tool correctly and understand its results.
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%, so the schema fully documents `script` and `product_url`. The description does not add parameter-level meaning beyond the schema, which matches the baseline expectation for full schema coverage.
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 specific verb ('Audit'), a concrete resource ('TikTok Shop or Amazon affiliate video script'), and enumerates the exact violation categories it detects. It also states the return values, making the tool's purpose unmistakable even without opening the schema or comparing to siblings.
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 usage context is implied: an agent can infer this tool should be used when a video script needs a policy-compliance check. However, it does not explicitly state when not to use it, nor does it mention the sibling tool generate_appeal or how the two relate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_appealGenerate platform violation appealAInspect
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.
ParametersJSON Schema
| 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. |
TDQS
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal negative hints, so the description carries most of the behavioral burden. It adds meaningful behavior by stating the output is 'ready-to-submit' and that it respects TikTok's 800-character limit. It does not promise appeal success or introduce any contradiction with 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 two concise sentences, front-loaded with the tool's purpose and followed by the key constraint and input instructions. Every sentence earns its place with no filler or redundant detail.
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?
For a two-parameter generation tool with no output schema, the description covers the purpose, the required inputs, and an important length constraint. It lightly implies rather than states the output format, but this is a minor gap given the low complexity and complete schema.
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%, so the baseline is 3. The description adds only surface-level guidance ('Paste the violation notice text... specify the platform') that mostly restates what the schema already documents. It does not introduce new parameter semantics.
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 states a specific verb ('Generate') and a clear resource ('a ready-to-submit appeal response for TikTok, Amazon, or YouTube policy violation notice'). It is easy to distinguish from the sibling audit_script because the purpose is explicit and scoped to three named platforms.
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?
The description gives clear usage context: paste the received violation notice and specify the platform. It provides the triggering condition for use without naming alternatives or exclusions, but the sibling tool is unrelated enough that exclusions are not needed here.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
- Changed
audit_script1 field changed- added
Input 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" +}
- Added
generate_appeal
1 tool update
- First observed
audit_script
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TDQS
A4.2/5.0
Disambiguation5/5
The two tools have completely distinct purposes: audit_script analyzes content for violations, while generate_appeal creates responses to violation notices. There is no overlap or ambiguity between them.
Naming Consistency5/5
Both tool names follow the same verb_noun pattern: audit_script and generate_appeal. The naming is clear, consistent, and predictable.
Tool Count3/5
With only 2 tools, the server feels thin for its stated purpose of 'ban-proofing.' While each tool is useful, the scope likely warrants additional tools such as rewrite_script or check_policy to fully support the workflow.
Completeness4/5
The audit tool covers policy violations and provides safe rewrites, and the appeal tool handles violation notices. Minor gaps exist (e.g., no tool to directly submit appeals or access policy details), but the core workflow of identify-and-appeal is covered.