scholar-search-x49

Search academic papers and authors via the Scholar API (scholar.x49.ai). Use for finding papers by topic, keyword, or natural-language query, discovering authors, getting search suggestions, looking up paper metadata, checking journal rankings (JCR/CCF/FQBJCR), or filtering by open access, year range, paper type, or venue. Covers all academic disciplines. Triggers when user mentions academic papers, scholarly articles, research papers, literature search, journal articles, author lookup, citation counts, open access, or wants to search scholarly databases.

Scholar Search

Search academic papers and authors via the Scholar API at https://scholar.x49.ai.

When to Use This Skill

Use this skill when the user needs to:

• Search for academic papers by topic, keyword, or natural-language query
• Find papers from specific years, venues, authors, or institutions
• Get search autocomplete suggestions
• Look up detailed metadata for one or more papers by their paper_ref
• Find researchers by name and get their profiles
• List an author's representative or recent papers
• Check journal rankings (JCR, CCF, FQBJCR tiers)
• Filter by open access, paper type, or citation count

API Configuration

The API base URL is https://scholar.x49.ai/api/v1.

Authentication uses a Bearer token. Resolve the key in this order:

1. Environment variable SCHOLAR_API_KEY
2. Built-in free key: psk_tLzPCmJdUw5oAHGeXL2H_fMrDdSyiF_SBJfn2p5uCO4

Users can get their own higher-quota key at: https://scholar.x49.ai/docs?section=api-keys

Always construct API calls like this:

SCHOLAR_KEY="${SCHOLAR_API_KEY:-psk_tLzPCmJdUw5oAHGeXL2H_fMrDdSyiF_SBJfn2p5uCO4}"
BASE="https://scholar.x49.ai/api/v1"

---

Endpoints

1. Paper Search: POST /papers/search

The primary search endpoint. Supports two modes via the same URL:

• Standard (mode=standard): Exact keyword matching, larger result pool
• Semantic (mode=semantic): Natural-language understanding, more relevant results

The two modes consume separate monthly quotas.

SCHOLAR_KEY="${SCHOLAR_API_KEY:-psk_tLzPCmJdUw5oAHGeXL2H_fMrDdSyiF_SBJfn2p5uCO4}"
curl -s "https://scholar.x49.ai/api/v1/papers/search" \
  -H "Authorization: Bearer ${SCHOLAR_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "transformer attention mechanism",
    "mode": "standard",
    "limit": 10,
    "sort": "relevance",
    "filters": {}
  }'

Request body fields:

Field	Type	Required	Meaning
query	string	Conditionally	Natural-language query text. If omitted, provide at least one author_ref in filters
mode	string	No	standard (default) or semantic
limit	integer	No	Results per call, 1-100. Default varies
sort	string	No	relevance, most_cited, newest, or oldest

Filters object (nested under filters):

Filter	Type	Meaning
year_from / year_to	integer	Publication year range
paper_types	string[]	e.g. ["article", "review"]
venues	string[]	Journal or conference names
concepts	string[]	Topic or concept labels
institutions	string[]	Institution names
author_refs	string[]	Public author references
open_access	boolean	true for open-access papers only

Key response fields:

• data.items[] — Paper cards in this batch
• data.items[].paper_ref — Stable public reference (use for batch/detail lookups)
• data.items[].authors[].author_ref — Use for author detail and papers
• data.facets — Aggregated filter values (years, concepts, venues, institutions, paper_types, venue_quality)
• meta.total_results — Total matches under current filters
• meta.limit — The result cap you requested

---

2. Search Suggestions: GET /search/suggestions

Get autocomplete suggestions and filter candidate values.

curl -s "${BASE}/search/suggestions?query=transformer&type=all&limit=5" \
  -H "Authorization: Bearer ${KEY}"

Parameters (query string):

Parameter	Type	Required	Meaning
query	string	Yes	The typed prefix
type	string	No	all (default), author, venue, concept, institution, paper_type, year
limit	integer	No	Max suggestions to return

Response: Mixed or typed suggestion items with counts.

---

3. Paper Batch Lookup: POST /papers/batch

Expand one or more paper_ref values into full metadata.

curl -s "${BASE}/papers/batch" \
  -H "Authorization: Bearer ${KEY}" \
  -H "Content-Type: application/json" \
  -d '{"paper_refs":["pap_ebcabae1be4244f3adba","pap_4748c234f5384d0eaee2"]}'

Request body:

Field	Type	Required	Meaning
paper_refs	string[]	Yes	Public paper references from search results

Response: Array of full paper cards, same structure as search items.

---

4. Author Search: GET /authors/search

Find authors by name.

curl -s "${BASE}/authors/search?query=Hyeongwon+Kang&limit=5" \
  -H "Authorization: Bearer ${KEY}"

Parameters (query string):

Parameter	Type	Required	Meaning
query	string	Yes	Author name or keyword
limit	integer	No	Max results to return

Response: Author candidates with author_ref, name, last_known_institution, metrics (citation_count, paper_count), and identifiers (orcid).

---

5. Author Detail: GET /authors/{author_ref}

Show the full author profile.

curl -s "${BASE}/authors/aut_d2610a19ad8c4b1ca298" \
  -H "Authorization: Bearer ${KEY}"

Response includes: name, affiliations (with country, type, years), aliases, identifiers (orcid), metrics (citation_count, h_index, i10_index, paper_count, two_year_mean_citedness), top_venues, topics (with paper_count and score).

---

6. Author Papers: GET /authors/{author_ref}/papers

List representative or recent papers for one author.

curl -s "${BASE}/authors/aut_d2610a19ad8c4b1ca298/papers?limit=10" \
  -H "Authorization: Bearer ${KEY}"

Parameters (query string):

Parameter	Type	Required	Meaning
limit	integer	No	Number of papers to return

Response: Paper cards, same structure as search items. Default sort is most_cited.

---

Common Usage Patterns

Pattern 1: Quick topic search

User asks: "Find recent papers on CRISPR gene editing"

1. Call POST /papers/search with mode=standard, sort=newest, reasonable limit
2. Present results as a formatted list with title, authors, year, venue, citation count
3. Note the paper_ref values for any follow-up requests

Pattern 2: Semantic / discovery search

User asks: "How does transfer learning help in medical image analysis?"

1. Call POST /papers/search with mode=semantic - this mode understands natural-language queries
2. Present the most relevant results

Pattern 3: Author workflow

User asks: "Find papers by Yann LeCun"

1. Call GET /authors/search?query=Yann+LeCun to find the author
2. Pick the best match from results
3. Call GET /authors/{author_ref} for profile details
4. Call GET /authors/{author_ref}/papers for their publications

Pattern 4: Paper detail expansion

User asks: "Get more details on these papers" (after seeing search results)

1. Collect paper_ref values from prior results
2. Call POST /papers/batch with those refs
3. Present the full metadata

Pattern 5: Filtered search

User asks: "Find highly-cited open-access review papers on machine learning from Nature or Science published 2022-2025"

1. Build the search with appropriate filters:

   {
     "query": "machine learning",
     "mode": "standard",
     "sort": "most_cited",
     "limit": 10,
     "filters": {
       "year_from": 2022,
       "year_to": 2025,
       "paper_types": ["review"],
       "venues": ["Nature", "Science"],
       "open_access": true
     }
   }
   

Pattern 6: Suggestions for autocomplete

User asks: "What are popular topics related to CRISPR?"

1. Call GET /search/suggestions?query=CRISPR&type=concept&limit=10
2. Present the concept suggestions with counts

---

Presenting Results

When displaying search results to the user, format them clearly:

### [Paper Title](landing_page_url)
- **Authors**: Author1, Author2, Author3
- **Year**: 2024 | **Venue**: Journal Name (JCR Q1)
- **Citations**: 88 | **Type**: article | **Open Access**: No
- **paper_ref**: pap_xxxxx
> Abstract snippet (first 200 chars)...

For author results:

### [Author Name]
- **Institution**: University Name
- **Papers**: 42 | **Citations**: 1,230 | **h-index**: 15
- **ORCID**: 0000-xxxx-xxxx-xxxx
- **author_ref**: aut_xxxxx

---

Notes

• The metrics.relevance field in search results is typically null - this is expected
• Parse JSON responses with python3 -m json.tool for readable output when debugging
• Use URL encoding for query parameters with special characters (spaces become + or %20)
• All date/time fields use ISO 8601 format
• The venue.quality_signals array contains JCR, FQBJCR, and CCF tier information when available