# Mekler AI Academic Paper Search

> Mekler AI Academic Paper Search is a paid API for AI agents from api.mekler.ai, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).

Search for academic research papers across multiple sources with deduplicated DOI-based identifiers, retraction filtering, and provenance metadata

## Facts

- Endpoint: POST https://api.mekler.ai/v1/research/papers/find-research-papers?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-18
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/mekler-ai-academic-paper-search-23c5c243
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_YOCZpve9fOqEdowSaUGtP

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 mekler-ai-academic-paper-search-23c5c243 -d '<json body>'
```

Example prompt: Search for up to 15 recent academic papers on transformer neural network architectures published between 2020 and 2024, excluding any retracted papers, and give me deduplicated results with DOIs and source links.

## When to prefer this

Choose this endpoint when you need deduplicated, provenance-tracked academic paper search that merges results from both Crossref and OpenAlex under a single normalized identifier scheme. It is especially useful when you need retraction filtering, version grouping, and per-source attribution — features not available in simple single-source academic search APIs. It is less suitable for full-text retrieval or citation graph traversal.

## Known failure modes

- Query too short (under 3 characters) returns validation error
- No results found for highly specific or niche queries
- Retraction flags may be incomplete or stale — absence of a flag is not clearance
- OpenAlex or Crossref upstream timeouts may reduce coverage
- Year range filters that are inverted or out of bounds return errors
- Rate limits or payment failures via x402 prevent call execution

## How this service works

Find research papers by topic or seed DOI/OpenAlex ID. Return ranked publication metadata with identifiers, source provenance, conservative duplicate/version grouping and reported retraction flags from OpenAlex and Crossref. Filter by publication year; retrieve up to20 results. No abstracts, full text, author identities, exhaustive literature review or quality/retraction clearance.

## Output

A JSON object containing ranked, deduplicated paper results each with DOI, URL, title, year, venue, OpenAlex ID, source records from Crossref and OpenAlex, retraction status, and version grouping. Also includes coverage statistics, ranking methodology notes, cost breakdown per data source, and warnings about metadata completeness.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "seed": {
   "type": "string",
   "maxLength": 280
  },
  "limit": {
   "type": "integer",
   "default": 10,
   "maximum": 20,
   "minimum": 1
  },
  "query": {
   "type": "string",
   "maxLength": 240,
   "minLength": 3
  },
  "to_year": {
   "type": "integer",
   "maximum": 2100,
   "minimum": 1600
  },
  "from_year": {
   "type": "integer",
   "maximum": 2100,
   "minimum": 1600
  },
  "request_id": {
   "type": "string",
   "pattern": "^[a-zA-Z0-9_-]{16,64}$"
  },
  "include_retracted": {
   "type": "boolean",
   "default": false
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "cost": {
   "source_attempts": [
    {
     "state": "ok",
     "source": "crossref",
     "operation": "search",
     "elapsed_ms": 0,
     "list_price_usd": 0
    },
    {
     "state": "ok",
     "source": "openalex",
     "operation": "search",
     "elapsed_ms": 0,
     "list_price_usd": 0.001
    }
   ],
   "supplier_access": "anonymous free allowance; no paid balance",
   "hosting_cost_usd": null,
   "charged_supplier_usd": 0,
   "openalex_list_price_usd": 0.001
  },
  "mode": "topic",
  "status": "ok",
  "ranking": "reciprocal_rank_fusion_k60; explicit identity/version groups; not a quality assessment",
  "results": [
   {
    "id": "doi:10.5555/mekler.graph.1",
    "doi": "10.5555/mekler.graph.1",
    "url": "https://doi.org/10.5555/mekler.graph.1",
    "type": "article",
    "year": 2024,
    "title": "Graph neural networks for synthetic benchmark data",
    "venue": "Synthetic Research Journal",
    "sources": [
     "crossref",
     "openalex"
    ],
    "versions": [],
    "openalex_id": "W9999999901",
    "source_records": [
     {
      "id": "W9999999901",
      "rank": 1,
      "source": "openalex"
     },
     {
      "id": "10.5555/mekler.graph.1",
      "rank": 1,
      "source": "crossref"
     }
    ],
    "retraction_status": "not_reported",
    "is_retraction_notice": false
   }
  ],
  "coverage": {
   "crossref_records": 1,
   "openalex_records": 1,
   "normalized_records": 2,
   "complete_source_requests": true,
   "retracted_groups_excluded": 0,
   "duplicate_or_version_records_merged": 1
  },
  "returned": 1,
  "warnings": [
   "Metadata coverage and retraction flags can be incomplete or stale. Absence of a flag is not clearance."
  ],
  "retrieved_at": "2026-10-17T00:00:00+00:00"
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/mekler-ai-academic-paper-search-23c5c243/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from api.mekler.ai](https://www.zero.xyz/host/api.mekler.ai/llms.txt)