biolit

mcp-name: io.github.rachadele/biolit

LLM-assisted biomedical literature screening and structured extraction. Accepts PubMed alert emails and mixed lists of PMIDs, DOIs, and GEO accessions in any combination. Retrieves full text from PMC, Europe PMC, bioRxiv/medRxiv, Unpaywall, and Semantic Scholar. Supports multiple LLM providers and exposes all functionality as an MCP server.

Setup

Requirements: Python 3.8+

Install from PyPI:

pip install biolit

Or install from source for development:

pip install -e .

Copy .env.example to .env and add your API key:

cp .env.example .env
# edit .env and set ANTHROPIC_API_KEY (or OPENAI_API_KEY)

On macOS, you can store the key in the system keychain instead of .env. biolit consults the keychain by service name only (no account required):

security add-generic-password -s ANTHROPIC_API_KEY -w
# or for OpenAI:
security add-generic-password -s OPENAI_API_KEY -w

Omit -w <value> to be prompted for the key without echoing it. The keychain is checked first; the env var is used only as a fallback (so a stale value in .env cannot mask a working keychain entry).

Usage

The tool accepts a PubMed alert email (.eml) or a plain-text file of identifiers, as well as inline identifiers via --ids. Identifiers can be PMIDs, DOIs, or GEO accessions — mixed lists are supported in a single run.

Use --default to run with schizophrenia genomics defaults (no prompts):

biolit docs/alert.eml --default
biolit docs/pmids.txt --default
biolit docs/geo_accessions.txt --default
biolit --ids 41795042,41792186,GSE53987 --default
biolit --ids 10.1101/2025.03.17.25324098 --default

Or specify criterion and fields as flags:

biolit identifiers.txt \
  --criterion "Is this about treatment-resistant schizophrenia?" \
  --fields "methodology, sample_size, treatment, outcomes"

Add --markdown (or --md) to also write a prose .md summary alongside the CSV. Each record gets a markdown section with ### field subsections; records that failed or were skipped appear as stub entries:

biolit refs.bib --config my_config.json --markdown
biolit refs.bib --config my_config.json --markdown --markdown-max-tokens 2048

Add --batch to issue screening, extraction, and markdown rendering through the provider's Message Batches / Batch API instead of one call per record. Per-request cost drops by ~50%, but each batch blocks on completion (typically several minutes per stage; up to 6 hours), so it's intended for the bulk weekly-alert case rather than one-off lookups. Anthropic and OpenAI only — falls back to sequential calls on Ollama or on OpenAI-compatible endpoints with a custom base_url. Also accepted as "batch": true in a config file.

biolit docs/alert.eml --default --batch
biolit docs/alert.eml --default --batch --markdown   # batches markdown too

Or use a JSON config file to store reusable parameters (CLI flags take precedence). The config can include ids or input_file (path to an .eml, .bib, or identifier list), and "markdown": true to enable markdown output:

biolit alert.eml --config my_config.json
biolit refs.bib --config my_config.json   # DOIs extracted from .bib automatically
biolit --config my_config.json            # ids or input_file supplied by config

The fields key in a config file can be a comma-separated string or a JSON object mapping field names to extraction descriptions. When a string is used, an extra LLM call converts the field names into descriptions before extraction. When a dict is used, that call is skipped — the descriptions are passed directly to the model:

{
  "fields": {
    "tf_name": "HGNC symbol of the transcription factor perturbed in this experiment",
    "organism": "scientific name of the organism used",
    "platform": "GPL accession of the microarray platform"
  }
}

Omit --criterion to skip screening (all records are extracted). Omit --fields to use the default fields (methodology, sample_type, causal_claims, summary):

# fetch + extract with defaults (no screening)
biolit alert.eml

# fetch + screen only, then extract with defaults
biolit alert.eml --criterion "Is this about treatment-resistant schizophrenia?"

Single-record screening

Use biolit screen to quickly check one paper or GEO record for relevance without running the full extraction pipeline:

biolit screen --pmid 41627908 --default
biolit screen --accession GSE53987 --default
biolit screen --doi 10.64898/2026.02.16.706214 --default
biolit screen --pmid 41627908 --criterion "Is this about treatment-resistant schizophrenia?"

Output is a single line to stdout:

RELEVANT [abstract] — Paper uses GWAS to investigate schizophrenia risk loci.

Mixed identifier lists

PMIDs, DOIs, and GEO accessions can be freely mixed in a file or via --ids. Each identifier is auto-detected by format:

• 41795042 → PMID (all digits)
• 10.1101/2025.03.17.25324098 → DOI (starts with 10.)
• GSE53987 → GEO accession (starts with GSE, GDS, GSM, or GPL)

biolit --ids 41795042,GSE53987,10.1101/2025.03.17.25324098 --default

GEO records additionally include a linked_pmids column. All record types share pmid, doi, and geo_accession columns (null when not applicable).

Full-text retrieval

Full-text retrieval runs automatically for every PMID and DOI (including preprints). For GEO records, the pipeline attempts full-text retrieval via each linked PMID in order, falling back to the GEO record metadata if no linked paper has accessible full text. The pipeline tries each source in order:

1. PMC JATS XML (open access)
2. Europe PMC JATS XML (broader open-access coverage)
3. Preprint XML (bioRxiv / medRxiv)
4. Unpaywall PDF (requires --unpaywall-email)
5. Semantic Scholar open-access PDF
6. OpenAlex green-OA PDF (author manuscripts Unpaywall/S2 miss; key-less)
7. Europe PMC open-access full-text PDF (OA subset)
8. CORE aggregated green-OA PDF (opt-in; needs CORE_API_KEY)
9. Publisher landing-page scrape (the citation_pdf_url meta tag; key-less)
10. Custom resolvers (institutional OpenURL / library proxy; opt-in via BIOLIT_CUSTOM_RESOLVERS)
11. Publisher landing-page HTML full text (the citation_fulltext_html_url meta tag; key-less)
12. Abstract fallback

Steps 6-9 are all open-access-only (green-OA author manuscripts, institutional-repository copies, and the publisher's own advertised OA PDF link) — never a paywall bypass. OpenAlex, Europe PMC, and the landing-page scrape need no key; CORE is a no-op unless CORE_API_KEY is set. The landing-page scrape (step 9) follows the DOI to the article page and reads the citation_pdf_url link the publisher itself embeds (the Highwire / Google-Scholar standard) — this catches OA PDFs the aggregator APIs mislabel or never index. bioRxiv / medRxiv are skipped there (their servers block agents; the preprint step above covers them). Step 10 is the seam for your own authorized access — see Custom full-text fetchers. Step 11 is the HTML counterpart of step 9: when no downloadable PDF exists at all, it extracts the article body text from the publisher's full HTML page (the citation_fulltext_html_url Highwire signal that PLOS / eLife / BMC / Frontiers and many society journals set) — recovering Methods text for OA papers the PDF chain can never reach. When even that misses, the abstract fallback records a paper_status classification (bot_blocked / js_shell / abstract) in the per-record artifacts so a caller can read why full text was not reached.

To enable Unpaywall (step 4), pass your email:

biolit alert.eml --default --unpaywall-email you@example.com

Limit which sections are sent to the LLM:

biolit alert.eml --default --sections methods,results

LLM providers

The tool supports Anthropic (default), OpenAI, and local Ollama models:

# OpenAI
biolit pmids.txt --default --provider openai --model gpt-4o

# Ollama (local)
biolit pmids.txt --default --provider ollama --model llama3

You can also set LLM_PROVIDER and LLM_MODEL as environment variables.

Output

Each run creates a timestamped directory (e.g. run_20260313_142000/) containing:

• results.csv — one row per relevant record
• results.md — prose markdown summary (written when --markdown or "markdown": true in config)
• artifacts/<id>/ — per-record folder with the text sent to the LLM, metadata, and any retrieved full-text files

Records that fail at any pipeline stage (fetch error, not found, no content, screening or extraction error) are excluded from the CSV but appear in the markdown as stub entries with a failure note.

With default fields, the CSV columns are: