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:

bash

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.

bash

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:

FieldTypeRequiredMeaningquerystringConditionallyNatural-language query text. If omitted, provide at least one author_ref in filtersmodestringNostandard (default) or semantic``limitintegerNoResults per call, 1-100. Default variessortstringNorelevance, most_cited, newest, or oldest

Filters object (nested under filters):

FilterTypeMeaningyear_from / year_tointegerPublication year rangepaper_typesstring[]e.g. ["article", "review"]``venuesstring[]Journal or conference namesconceptsstring[]Topic or concept labelsinstitutionsstring[]Institution namesauthor_refsstring[]Public author referencesopen_accessbooleantrue 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.

bash

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

Parameters (query string):

ParameterTypeRequiredMeaningquerystringYesThe typed prefixtypestringNoall (default), author, venue, concept, institution, paper_type, year``limitintegerNoMax 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.

bash

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

Request body:

FieldTypeRequiredMeaningpaper_refsstring[]YesPublic 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.

bash

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

Parameters (query string):

ParameterTypeRequiredMeaningquerystringYesAuthor name or keywordlimitintegerNoMax 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.

bash

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.

bash

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

Parameters (query string):

ParameterTypeRequiredMeaninglimitintegerNoNumber 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: json

   {
     "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:

text

### [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:

text

### [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