Mendeley REST API

SkillSearch

Manage references and search Mendeley's catalog via REST API

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Mendeley REST API skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/43-wentorai-research-plugins/skills/writing/citation/mendeley-api/SKILL.md and read by ahel’s review.

Overview

Mendeley provides a reference management platform with a REST API for programmatic access to personal libraries, group collections, and the Mendeley Catalog — a crowdsourced database of 200M+ academic documents. The API supports OAuth 2.0 authentication, CRUD operations on documents/folders/annotations, and catalog search with rich metadata. Free tier available with registration.

Authentication

Mendeley uses OAuth 2.0 with client credentials or authorization code flow.

# 1. Register app at https://dev.elsevier.com/
# 2. Get access token via client credentials (for catalog search)
curl -X POST "https://api.mendeley.com/oauth/token" \
  -d "grant_type=client_credentials" \
  -d "scope=all" \
  -d "client_id=$MENDELEY_CLIENT_ID" \
  -d "client_secret=$MENDELEY_CLIENT_SECRET"

# Response: { "access_token": "...", "expires_in": 3600, "token_type": "bearer" }

API Endpoints

Base URL

https://api.mendeley.com

Catalog Search

Search across Mendeley's 200M+ document database:

# Search by title/keywords
curl -H "Authorization: Bearer $TOKEN" \
  "https://api.mendeley.com/catalog?query=deep+learning+NLP&limit=20"

# Search by DOI
curl -H "Authorization: Bearer $TOKEN" \
  "https://api.mendeley.com/catalog?doi=10.1038/nature14539"

# Search by title
curl -H "Authorization: Bearer $TOKEN" \
  "https://api.mendeley.com/catalog?title=attention+is+all+you+need"

User Library

# List documents in personal library
curl -H "Authorization: Bearer $TOKEN" \
  "https://api.mendeley.com/documents?limit=50&sort=created&order=desc"

# Get document details
curl -H "Authorization: Bearer $TOKEN" \
  "https://api.mendeley.com/documents/{doc_id}"

# Add document to library
curl -X POST -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/vnd.mendeley-document.1+json" \
  -d '{"title":"My Paper","type":"journal","year":2025,"authors":[{"first_name":"A","last_name":"B"}]}' \
  "https://api.mendeley.com/documents"

Folders and Groups

# List folders
curl -H "Authorization: Bearer $TOKEN" \
  "https://api.mendeley.com/folders"

# List group documents
curl -H "Authorization: Bearer $TOKEN" \
  "https://api.mendeley.com/documents?group_id={group_id}"

Annotations

# Get annotations for a document
curl -H "Authorization: Bearer $TOKEN" \
  "https://api.mendeley.com/annotations?document_id={doc_id}"

Query Parameters

ParameterDescriptionExample
queryFree-text searchquery=transformer+model
doiDOI lookupdoi=10.1234/example
titleTitle searchtitle=BERT
authorAuthor filterauthor=LeCun
min_yearFrom yearmin_year=2020
max_yearTo yearmax_year=2026
limitResults per page (max 500)limit=50
sortSort fieldcreated, title, year
orderSort directionasc or desc
viewResponse detailbib (bibliographic), stats (reader counts)

Catalog Response

{
  "id": "abc123-...",
  "title": "Attention Is All You Need",
  "type": "conference_proceedings",
  "year": 2017,
  "authors": [
    {"first_name": "Ashish", "last_name": "Vaswani"}
  ],
  "source": "NeurIPS",
  "identifiers": {
    "doi": "10.5555/3295222.3295349",
    "arxiv": "1706.03762"
  },
  "keywords": ["attention mechanism", "transformer"],
  "abstract": "The dominant sequence transduction models...",
  "reader_count": 15432,
  "link": "https://www.mendeley.com/catalogue/..."
}

Python Usage

import os
import requests

CLIENT_ID = os.environ["MENDELEY_CLIENT_ID"]
CLIENT_SECRET = os.environ["MENDELEY_CLIENT_SECRET"]
TOKEN_URL = "https://api.mendeley.com/oauth/token"
BASE_URL = "https://api.mendeley.com"


def get_token() -> str:
    """Obtain access token via client credentials."""
    resp = requests.post(TOKEN_URL, data={
        "grant_type": "client_credentials",
        "scope": "all",
        "client_id": CLIENT_ID,
        "client_secret": CLIENT_SECRET,
    })
    resp.raise_for_status()
    return resp.json()["access_token"]


def search_catalog(query: str, limit: int = 20,
                   min_year: int = None) -> list:
    """Search the Mendeley catalog."""
    token = get_token()
    params = {"query": query, "limit": limit, "view": "bib"}
    if min_year:
        params["min_year"] = min_year

    resp = requests.get(
        f"{BASE_URL}/catalog",
        headers={"Authorization": f"Bearer {token}"},
        params=params,
    )
    resp.raise_for_status()

    results = []
    for doc in resp.json():
        results.append({
            "title": doc.get("title"),
            "authors": [f"{a['first_name']} {a['last_name']}"
                        for a in doc.get("authors", [])],
            "year": doc.get("year"),
            "source": doc.get("source"),
            "doi": doc.get("identifiers", {}).get("doi"),
            "readers": doc.get("reader_count", 0),
        })
    return results


def lookup_by_doi(doi: str) -> dict:
    """Look up a single document by DOI."""
    token = get_token()
    resp = requests.get(
        f"{BASE_URL}/catalog",
        headers={"Authorization": f"Bearer {token}"},
        params={"doi": doi, "view": "bib"},
    )
    resp.raise_for_status()
    items = resp.json()
    return items[0] if items else {}


# Example
papers = search_catalog("federated learning privacy", min_year=2023)
for p in papers:
    print(f"[{p['year']}] {p['title']} — readers: {p['readers']}")

Reader Statistics

Mendeley tracks how many users have saved each paper, providing a real-time measure of scholarly interest (unlike citation counts which lag by months).

def get_popular_papers(topic: str, limit: int = 10) -> list:
    """Find most-read papers on a topic via reader counts."""
    results = search_catalog(topic, limit=limit)
    return sorted(results, key=lambda x: x["readers"], reverse=True)

Rate Limits

TierRequests/hourCatalog access
Free150Read-only catalog + personal library
InstitutionalHigherFull API access

References

Signals

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Last commit
Sep 2026

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Item type
skill
Key
mendeley-api
Source
github.com/brycewang-stanford/auto-empirical-research-skills