BanProof AI
Server Details
Audit creator scripts for TikTok Shop and Amazon policy violations. Returns flags and safe rewrites.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- NimishRangani/banproof-mcp
- GitHub Stars
- 0
- Server Listing
- banproof-mcp
Available Tools
1 toolaudit_scriptAudit script for policy violationsARead-onlyIdempotentInspect
Audit 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. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds value by disclosing the analysis dimensions (medical claims, guarantees, false certifications, unproven efficacy) and the return payload (flagged phrases, reasons, safe rewrites, risk level). No contradiction.
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-load the core action and scoping, then list outputs. There is no filler or repetition of schema content.
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 single-parameter, read-only tool with no output schema, the description supplies all needed context: target platform, violation categories, return values, and risk level. Annotations cover mutability/idempotence and the schema covers the parameter.
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?
The schema covers 100% of parameters: the required 'script' field is described as 'The full video script text to audit.' The description adds context about the script type but not additional parameter semantics, so the baseline 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 uses a specific verb ('Audit'), identifies the exact resource ('TikTok Shop or Amazon affiliate video script'), names policy categories, and states the outputs. With no sibling tools, differentiation is not required.
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?
It clearly scopes the tool to affiliate video scripts for TikTok Shop/Amazon policy review. It does not offer explicit when-not-to-use guidance, but with no siblings and a clear professional context, the intended use is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Frequently Asked Questions
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as
io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Connectors
Static linter for CLAUDE.md-style agent constitution files: 10 operational-guardrail checks.
Static linter for CLAUDE.md-style agent constitution files: 10 operational-guardrail checks.
Check AI work against requirements and return structured verdicts, findings, and repair steps.
Sentiment, toxicity, entity extraction, PII, translation, summary, QA, fraud scoring, safety audit.
Related MCP Servers
- AlicenseAqualityBmaintenanceChecks 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.1204MIT
- AlicenseAqualityCmaintenanceHelps UGC creators interpret brand briefs, generate shot lists and deliverables, and identify red flags before production.754MIT
- AlicenseAqualityCmaintenanceProvides AI agents with structured short-form content mechanics including hooks, script structures, retention strategies, and CTAs, along with auditing tools to avoid common posting failures.761MIT
- AlicenseAqualityBmaintenanceAudits MCP tool descriptions for quality and reliability, scoring them 0-100, detecting smells, and providing rewritten versions for better agent accuracy.4MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
There is only one tool, so there is no possibility of an agent confusing it with another tool. Its purpose is clearly and narrowly defined.
The single tool name follows a clear verb_noun convention. With only one name, there are no conflicting naming styles or patterns to confuse an agent.
One tool is borderline: it covers the core audit action, but the server feels very thin. Additional related operations such as batch audits or historical audit retrieval would make the server feel more complete.
The audit tool fully covers its stated core workflow: flagging violations, explaining them, suggesting rewrites, and giving a risk level. Minor gaps exist around policy configuration and audit history, but they are not required for the primary use case.