Pay-per-call profanity/explicit-content detection for AI agents. $0.005 USDC per call, no signup.
Glama couldn't complete the latest health check. If this server requires authentication, missing or expired test credentials may be the cause. A test profile lets Glama authenticate for health checks and discover tools; it is separate from your personal connections.
If you are the author, claim ownership, then add or update a test profile under Admin → Test Profile.
Scored across 1 tool
Only a single tool exists, so there is no possibility of confusing it with other tools. The tool's purpose is clear and distinct.
The single tool follows a clear verb_noun pattern ('moderate' + 'text'), which is consistent and predictable. With only one tool, naming consistency is trivially satisfied.
The server has only 1 tool, which feels too thin for an API named 'Content Moderation API'. A typical content moderation service would include multiple modalities or operations, so the count is below what the scope suggests.
The tool only handles text profanity/explicit terms, leaving out other content types (images, video, audio) and other moderation aspects (spam, sentiment, policy compliance). This is a significant gap for a 'Content Moderation API'.
Check text for profanity/explicit terms. Costs $0.005 USDC on Base.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the disclosure burden. It transparently reveals that the call costs $0.005 USDC on Base, a non-obvious side effect of invoking the tool. However, it does not describe the result shape, error behavior, or whether text is transmitted/stored.
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?
One sentence, front-loads the purpose, and adds the cost detail without waste. Every word contributes.
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 paid external tool with no output schema, the missing return-value contract is a significant gap. The agent knows what the tool does and what it costs but not what a successful response looks like or how to interpret the moderation result.
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 coverage is 0% and the schema only provides the title 'Text'. The description adds that the text is checked for profanity/explicit terms, but does not specify expected format, length limits, language, or plain-vs-HTML handling, so it only partially compensates.
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 ('Check') and resource ('text') and names the exact purpose: detecting profanity/explicit terms. There are no sibling tools to differentiate against, so no differentiation is needed; it is clear and unambiguous.
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 intended use is implied by the purpose: use when text needs profanity/explicit-term moderation. There are no explicit when-to-use/when-not-to-use conditions or alternatives, but the cost disclosure hints that it should be called with awareness of the charge.
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.
moderate_textPay-per-call safety checks for AI agents: screen a crypto address or URL before you transact.
Pre-trade token safety checks for AI agents on Solana and Base. x402 USDC per call, no key.
Pay-per-call Open Graph/link preview metadata for AI agents. $0.01 USDC per call, no signup.
Pay-per-call checks for AI agents: sanctions, MiCA, French KYB, AI Act. USDC via x402.
Add one secure layer between your agents and this server.
