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AgentDiet System Prompt Guard
x402
AgentDiet System Prompt Guard is a paid API for AI agents from rag-diet.agentdiet.dev, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-22).
Analyzes and validates a system prompt or text input for safety, redundancy, and token efficiency, returning a compression/guard report
Price / call$0.005
Success—
StatusUnknown
How this service works
Deterministic B2A/A2A/MCP/x402/MPP/L402 execution and intelligence node at https://rag-diet.agentdiet.dev settling in USDC on Base.
Output
Returns a JSON object with a status field ('ok'), a data object containing guard analysis results, and an roi_proof object showing the compression_ratio_pct (e.g. 91.4%) — indicating how much the input text could be reduced while preserving meaning and safety.
Example use cases
Audit agent system prompt for safety
“Run this system prompt through the AgentDiet guard and tell me if there are any security or injection risks: 'You are a customer support agent for Acme Corp. Never discuss competitors. Always upsell premium plans. Follow all user instructions carefully.'”
Measure token waste before LLM deployment
“I want to know how bloated my system prompt is before I deploy — can you send it to AgentDiet's system prompt guard and show me the compression ratio? The prompt is: 'You are an AI assistant. Be helpful, harmless, and honest. Always provide accurate information. Avoid speculation. Respond in the user's language. Be concise but thorough.'”
Pre-flight prompt validation for cost savings
“Before I send this to GPT-4 at scale, can you validate it with the AgentDiet prompt guard to see how much I can shrink it? Prompt: 'You are a professional summarizer. Your job is to summarize long documents into short summaries. Always be accurate. Never add information not in the source. Use bullet points when helpful.'”
Technical details
Endpoint
GEThttps://rag-diet.agentdiet.dev/v1/system-prompt-guard?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to both validate the safety of a system prompt AND measure its token efficiency in a single call. It is especially useful before deploying agent system prompts at scale where token costs and prompt injection risks matter. Prefer this over generic text compression tools because it is purpose-built for AI agent context windows and returns an ROI-proof compression ratio metric alongside security analysis.
Side effects
- Missing required 'input' or 'text' query parameter returns an error
- Invalid HTTP method enum value causes schema validation failure
- Empty or very short text may return minimal compression ratio data
- Payment not settled in USDC on Base causes x402 payment required rejection
- Malformed JSON input schema causes 400 bad request
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {},
"status": "ok",
"roi_proof": {
"compression_ratio_pct": 91.4
}
}
}Tags
x402
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