# AgentDiet AI Paper Radar

> AgentDiet AI Paper Radar is a paid API for AI agents from status.agentdiet.dev, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-22).

Fetches and distills AI research paper intelligence, returning compressed summaries with token-reduction proof for agent context windows.

## Facts

- Endpoint: GET https://status.agentdiet.dev/v1/ai-paper-radar?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-22
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/agentdiet-ai-paper-radar-c7704694
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_1uZ00-EYxE_YOhTQrjZ_U

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability agentdiet-ai-paper-radar-c7704694
```

Example prompt: Can you pull up the latest AI paper radar from AgentDiet and give me a compressed digest of what's trending in AI research right now?

## When to prefer this

Choose this endpoint when you need token-efficient AI research paper intelligence delivered to an agent's context window, especially when context budget is tight and you want provable compression ratios. Useful for agents that need to stay current on AI research without blowing up their token budget. Not ideal for full-text paper retrieval or citation management.

## Known failure modes

- Missing or malformed 'input' object returns a validation error
- Invalid HTTP method in payload causes rejection
- Payment not settled in USDC on Base results in 402 response
- Empty query returns no results or generic response
- Rate limiting if too many calls are made in rapid succession

## How this service works

Deterministic B2A/A2A/MCP/x402/MPP/L402 execution and intelligence node at https://status.agentdiet.dev settling in USDC on Base.

## Output

Returns a JSON object with a 'data' field containing distilled AI paper content, a 'status' field confirming success, and an 'roi_proof' object showing the compression ratio achieved (e.g. 91.4% token reduction), demonstrating how much context was saved relative to raw input.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "required": [
  "input"
 ],
 "properties": {
  "input": {
   "type": "object",
   "required": [
    "type",
    "method"
   ],
   "properties": {
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "enum": [
      "GET",
      "POST"
     ],
     "type": "string"
    },
    "queryParams": {
     "type": "object",
     "properties": {
      "query": {
       "type": "string"
      }
     }
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type"
   ],
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object"
    }
   }
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "data": {},
  "status": "ok",
  "roi_proof": {
   "compression_ratio_pct": 91.4
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/agentdiet-ai-paper-radar-c7704694/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from status.agentdiet.dev](https://www.zero.xyz/host/status.agentdiet.dev/llms.txt)