Google Scholar Search

Google Scholar Search

Search Google Scholar for academic papers and author profiles to build a candidate list before triaging.

When to Use

• Discovering papers on a topic before running triage-paper
• Finding papers by a specific author, optionally filtered by year range
• Scoping a research area and exporting a candidate list to JSON for batch review

When Not to Use

• The paper is already known (arxiv ID, DOI, URL) — go straight to triage-paper
• The semantic-scholar MCP is configured — optionally prefer it; it returns structured data with no rate-limit risk
• The search is for biomedical literature — use PubMed or a domain-specific source
• A candidate JSON already exists at /tmp/<topic>-candidates.json — reuse it rather than re-running

Mindset

Search is discovery, not triage. The goal is a candidate list, not a finished reference.

1. Return candidates, not conclusions: hand the list to the user; let them choose what to triage.
2. Scraping is fragile: a gotcha is that a blank result may be a silent CAPTCHA redirect, not an empty query. Always verify by checking the raw response length before reporting "no results".
3. MCP first: a common pitfall is invoking the script without checking whether a semantic-scholar or google-scholar MCP is available. MCPs are faster, structured, and avoid rate-limit risk.

Workflow

1. Check MCP availability

Consider the semantic-scholar or google-scholar MCP server first. If either is configured and reachable, prefer it over this script.

2. Set up the environment (first run only)

See setup-and-troubleshooting.md for venv creation and dependency installation.

3. Run the search

Activate the venv, then choose the appropriate subcommand:

# Basic keyword search
./scripts/google-scholar-search.py search --query "retrieval-augmented generation" --results 10

# Advanced: filter by author and year range
./scripts/google-scholar-search.py advanced \
  --query "LLM reasoning" --author "Yann LeCun" \
  --year-start 2020 --year-end 2024 --results 10

# Author profile
./scripts/google-scholar-search.py author --name "Geoffrey Hinton"

# Export candidate list to JSON
./scripts/google-scholar-search.py search \
  --query "prompt compression" --format json --output /tmp/candidates.json

4. Parse JSON output (optional)

# Preview titles from a saved candidate list
python3 -c "import json; [print(p['title']) for p in json.load(open('/tmp/candidates.json'))]"

5. Handle blocked responses

If the script returns HTTP 429 or an unusually short HTML body, wait 60 seconds and retry once:

sleep 60 && ./scripts/google-scholar-search.py search --query "<topic>" --results 10

NEVER retry in a tight loop. If still blocked, offer the semantic-scholar MCP as fallback and document the blocker.

6. Present candidates and hand off

Show the result list. For each promising paper, offer:

Found N results. Would you like to triage any of these with triage-paper?

NEVER triage automatically — always confirm with the user first.

Anti-Patterns

NEVER auto-triage search results

WHY: Discovery and triage are separate quality gates. Auto-triaging bypasses user review.

BAD Run triage-paper on every result. → GOOD Present the list; wait for the user to choose.

NEVER loop on blocked responses

WHY: Repeated retries worsen the block and may trigger a longer ban.

BAD Retry in a tight loop. → GOOD Retry once after 60 s; switch to MCP fallback on second failure.

NEVER treat scraped abstracts as canonical

WHY: Scraped text is truncated and misformatted. Passing it to triage-paper corrupts the research record.

BAD Use the scraped abstract as the paper summary. → GOOD Use results for discovery only; re-fetch from arxiv or DOI during triage.

NEVER skip the MCP availability check

WHY: The script is the fragile fallback. Skipping the check needlessly risks rate-limiting.

BAD Invoke the script without checking for an MCP first. → GOOD Check for semantic-scholar/google-scholar MCP; only fall back to the script if neither is available.

NEVER treat year-range results as exhaustive

WHY: Google Scholar's year filter is a ranking hint, not a strict predicate. Papers outside the range may appear; papers inside may be missing.

BAD Claim "all 2020–2024 papers on X". → GOOD Qualify results as "a sample in the requested range, as returned by Google Scholar."

NEVER report "no results" without checking for a silent block

WHY: A 200 response with a near-empty body is often a CAPTCHA redirect, not a genuine empty result set.

BAD Report "no papers found" when the HTML body is unusually short. → GOOD Check response length; if under ~1 KB, treat it as a potential block and retry once.

References

• Setup & troubleshooting: setup-and-troubleshooting.md
• Script source: google-scholar-search.py
• Dependencies: requirements.txt
• Upstream: JackKuo666/google-scholar-search-skill
• Preferred alternative: see triage-paper for semantic-scholar MCP config