Audit TikTok Shop & Amazon affiliate scripts for policy violations via MCP.
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?
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.
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.
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.
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.
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.
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.
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?
The description adds useful behavioral context beyond annotations by noting that output is 'ready-to-submit' and that it 'stays within TikTok's 800-character appeal limit.' However, the annotations are all false/negative and provide little guidance, and the description does not disclose output structure, platform-specific formatting, or the need for user review before submission.
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 appropriately sized at three short sentences. The primary purpose is front-loaded, followed by a key output constraint and then direct usage guidance. Every sentence earns its place with no filler.
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 two-parameter generation tool with no output schema, the description covers the essential context: what it generates, for which platforms, the character-limit constraint, and how to invoke it. It could be more complete by explicitly describing the output format or noting that generated appeals may require review, but the core information needed to call it correctly is present.
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 the baseline is 3 even without additional parameter explanations in the description. The description's 'paste the violation notice text' and 'specify the platform' only echo the schema and add no new semantic detail about either parameter.
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 clearly states a specific verb and resource: 'Generate a ready-to-submit appeal response' for policy violation notices on TikTok, Amazon, or YouTube. It is immediately distinguishable from the sibling tool audit_script, which covers a different domain entirely.
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 gives clear usage context: use this tool when the user has received a platform policy violation notice and needs an appeal response. It instructs the user to paste the notice and specify the platform, but it does not explicitly state when not to use it or mention alternatives.
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. Dates show when Glama detected each change.
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_scriptClaiming 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, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on 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.
No comments yet. Be the first to start the discussion!
Add one secure layer between your agents and this server.
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.
Both tool names follow the same verb_noun snake_case pattern: audit_script and generate_appeal. The naming is predictable and consistent.
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.
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.