pantheon-org/pubmed-search

PubMed Search

Search and analyze biomedical literature from PubMed using the free NCBI E-utilities API.

When to Use

• Discovering biomedical or clinical papers by keyword, author, journal, or date range
• Fetching structured metadata (title, authors, abstract, DOI) for a known PMID
• Performing deep analysis of a paper when only the abstract and metadata are available
• Downloading open-access full-text PDFs from PubMed Central (PMC)
• Building a candidate list before running triage-paper

When Not to Use

• The paper is already known (DOI, URL) — go straight to triage-paper
• A semantic-scholar MCP or PubTator MCP is configured — prefer the MCP; it returns structured data with no rate-limit risk
• The search is for general academic literature — use google-scholar-search or semantic-scholar-search
• A candidate JSON already exists at /tmp/<topic>-candidates.json — reuse it

Recommended MCP Server

When available, prefer the PubTator MCP server over this script:

{
  "mcpServers": {
    "pubtator": {
      "type": "stdio",
      "command": "uvx",
      "args": ["pubtator-mcp-server"]
    }
  }
}

Mindset

Search is discovery, not analysis. The goal is a structured candidate list.

1. Rate limits are a gotcha: without an API key the limit is 3 req/s; a pitfall is issuing bulk PMID fetches without a delay, causing silent failures or 429s. ALWAYS add a short delay between batch calls.
2. MCP first: ALWAYS check whether a PubTator or PubMed MCP is configured before invoking the Python script. MCPs are faster, structured, and avoid rate-limit risk.
3. Open access is not guaranteed: a pitfall is assuming all PMC articles can be downloaded. NEVER attempt to download a PDF without first confirming PMC availability and open-access status.

Workflow

1. Check MCP availability

ALWAYS check for a pubtator or pubmed MCP before running the script. If configured and reachable, prefer it.

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. You may optionally add --show-abstract to keyword searches for a richer preview.

# Basic keyword search
./scripts/pubmed_search.py search --keywords "CRISPR gene editing" --results 10
# Advanced: filter by author, journal, and date range
./scripts/pubmed_search.py search --term "cancer immunotherapy" --author "Smith" \
  --journal "Nature" --start-date "2021" --end-date "2024" --results 20
# Fetch metadata for a known PMID
./scripts/pubmed_search.py metadata --pmid "33303479" --format json
# Deep paper analysis
./scripts/pubmed_search.py analyze --pmid "33303479" --output analysis.md
# Download open-access PDF
./scripts/pubmed_search.py download --pmid "33303479" --output-dir ./papers/
# Export candidate list to JSON
./scripts/pubmed_search.py search --keywords "Alzheimer disease biomarkers" \
  --results 50 --format json --output /tmp/candidates.json

4. Handle rate-limit errors

If the script returns HTTP 429, wait 30 s and retry once:

sleep 30 && ./scripts/pubmed_search.py search --keywords "<topic>" --results 10

NEVER retry in a tight loop. If still failing, set PUBMED_API_KEY in the environment.

5. Present candidates and hand off

NEVER triage automatically — ALWAYS confirm with the user first:

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

Anti-Patterns

NEVER auto-triage search results

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

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

NEVER retry in a tight loop on 429

WHY: Repeated rapid retries worsen the block and extend the cooldown period.

BAD Loop search until it succeeds. → GOOD Retry once after 30 s; switch to MCP or API key on second failure.

NEVER assume all PMC articles are downloadable

WHY: Many PMC articles are not open access; the download command will fail or return a redirect link.

BAD Call download for every PMID. → GOOD Check PMC availability and open-access status before downloading.

NEVER skip the MCP availability check

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

BAD Invoke the script without checking for a PubTator or PubMed MCP. → GOOD ALWAYS check MCP availability first; only fall back to the script if no MCP is configured.

NEVER hardcode API keys in scripts or task files

WHY: API keys committed to source are a production security risk and will be rotated or revoked.

BAD Set api_key = "abc123" inside the script. → GOOD ALWAYS use environment variables (PUBMED_API_KEY) or a .env file that is gitignored.

NEVER treat abstract-based analysis as a full-text review

WHY: PubMed metadata contains only the abstract. Deep analysis based solely on abstracts is incomplete and a pitfall for research quality.

BAD Mark a paper as "fully analysed" from analyze output alone. → GOOD Qualify analysis as "abstract-based" and recommend obtaining the full text for production use.

References

• Setup: setup-and-troubleshooting.md
• Script: pubmed_search.py
• Dependencies: requirements.txt
• Upstream: JackKuo666/pubmed-search-skill
• MCP alternative: PubTator-MCP-Server