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. Abstract fallback

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:

GEO records additionally include a linked_pmids column listing all associated PubMed IDs.

The CSV can be imported directly into Google Sheets (File → Import).

MCP server

biolit ships an MCP server that exposes the pipeline as tools for any MCP-compatible client (Claude Desktop, Claude CLI, OpenAI Agents SDK, etc.).

Start the server:

biolit-mcp
# or pick a provider/model explicitly (overrides LLM_PROVIDER / LLM_MODEL env vars):
biolit-mcp --provider openai --model gpt-4o-mini

Or test interactively with the MCP inspector:

mcp dev biolit/mcp_server.py

Configure Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "biolit": {
      "command": "biolit-mcp",
      "args": ["--provider", "openai"]
    }
  }
}

Restart Claude Desktop. The tools will appear in the tool picker. Drop args to use the default Anthropic provider (or set LLM_PROVIDER / LLM_MODEL env vars instead).

Configure Claude CLI

Add a .mcp.json in your project root:

{
  "mcpServers": {
    "biolit": {
      "command": "biolit-mcp",
      "args": ["--provider", "openai"]
    }
  }
}

Available tools

Batch pipeline (equivalent to the biolit CLI):

Low-level (for custom workflows):

Use as a Python library

The pipeline functions are importable directly:

from biolit.pipeline import run, screen_paper, fetch_record
from biolit.llm import get_llm_client

client = get_llm_client("anthropic")

# Batch pipeline — PMIDs, DOIs, and GEO accessions can be mixed freely
# criterion and fields_description are optional; omit either to skip that step
# markdown=True writes results.md alongside the CSV
# Returns (csv_path, record_count)
csv_path, count = run(client, ids=["41627908", "GSE53987", "10.1101/2025.03.17.25324098"],
    criterion="...", fields_description="methodology, summary", output_path="results.csv",
    markdown=True)

# Fetch + write metadata only (no LLM calls)
csv_path, count = run(client, ids=["41627908", "GSE53987"])

# Fetch a single record (auto-detects PMID / DOI / GEO)
paper = fetch_record("10.1101/2025.03.17.25324098")

# Screen pre-fetched text
result = screen_paper(client, paper, "Is this about schizophrenia genomics?", paper["abstract"])
# {"relevant": True, "reason": "..."}

Custom full-text fetchers

The built-in chain (PMC → Europe PMC → preprint → Unpaywall → Semantic Scholar → abstract) leaves coverage gaps for closed-access or recently-published work. You can plug in additional sources of full text — a Zotero library, a flat directory of PDFs, an institutional full-text database — without forking biolit.

Reference fetchers (opt-in via env vars)

Three ship with biolit and self-register on import when the relevant environment variables are set. Default priorities (lower = tried earlier) are bibtex=2.0, local_pdf=3.0, zotero=5.0.

BibTeX. Looks up papers by DOI, PMID, or citekey in a .bib export, reads the path from each entry's file = {...} field, and parses the PDF directly. Best fit for users who maintain a Better-BibTeX (or equivalent) auto-export — lookups are offline, in-memory, and exact, with no network round-trip and no dependence on the Zotero search index. Works around the Zotero web API's q-search not indexing the structured DOI field, which makes DOI lookups via that API unreliable for items where the DOI doesn't appear in indexed attachment full-text. Supports both BBT semicolon-separated file lists and the classic JabRef description:path:type format. The bib file is re-parsed automatically when its mtime changes.

export BIOLIT_BIBTEX=~/Zotero/My\ Library.bib
# Optional:
export BIOLIT_BIBTEX_PRIORITY=2.0   # lower = tried earlier (default 2.0)

Zotero. Searches the user's Zotero library by DOI then PMID, resolves attachment search hits up to their parent items, finds an attached PDF, downloads it, and parses it with biolit's PDF parser. When the Zotero /file API endpoint returns 404 (linked_file attachments, or imported attachments on accounts without sync), falls back to reading the PDF from local Zotero storage at $ZOTERO_DATA_DIR/storage/<key>/<filename> (default data dir ~/Zotero). Note: Zotero's web API q-search does not index the structured DOI field, so the BibTeX fetcher above is more reliable when both are available.

export ZOTERO_API_KEY=...
export ZOTERO_USER_ID=...           # or ZOTERO_GROUP_ID for a group library
# Optional:
export ZOTERO_PRIORITY=5.0          # lower = tried earlier (default 5.0)
export ZOTERO_DATA_DIR=~/Zotero     # only needed if Zotero is not at ~/Zotero

On macOS, any of ZOTERO_API_KEY, ZOTERO_USER_ID, and ZOTERO_GROUP_ID that are not in the environment fall back to the macOS keychain (security find-generic-password -s <NAME> -w), matching the resolution order used for LLM API keys. This means hosts like Claude Code that don't shell-source your profile can still pick up Zotero credentials without an env block in .mcp.json.

Local PDF directory. Looks up papers by DOI in a pre-built JSON index. Filenames are arbitrary — DOIs are extracted from each PDF's /Info metadata dict and (failing that) its first-page text.

Build (or update) the index. Re-running is cheap — by default only new or changed PDFs are re-extracted:

python -m biolit.fetchers.local_pdf --dir ~/Papers
python -m biolit.fetchers.local_pdf --dir ~/Papers --rebuild   # force full re-extraction

Then point biolit at the same directory:

export BIOLIT_LOCAL_PDF_DIR=~/Papers
export BIOLIT_LOCAL_PDF_PRIORITY=3.0  # default 3.0

The fetcher itself never builds the index — it only consults it. PDFs without an extractable DOI are listed in the index's unindexed_sample for visibility.

When configured, the text_source field in CSV/markdown output is bibtex_pdf, zotero_pdf, or local_pdf for hits from these sources. The raw bytes are persisted into artifacts/<id>/bibtex_pdf / zotero_pdf / local_pdf exactly like the built-in PMC/Europe PMC artifacts.

Writing your own fetcher

A fetcher is any callable that takes a FetchContext and returns either a FetchResult (when it found something) or None (when it didn't).

from biolit.fetchers import FetchContext, FetchResult, register_fetcher

def my_internal_db_fetcher(ctx: FetchContext) -> FetchResult | None:
    pmid = ctx.paper.get("pmid")
    if not pmid:
        return None
    text = my_db.lookup_fulltext(pmid)  # whatever you have
    if not text:
        return None
    return FetchResult(text=text, source="internal_db", artifacts={})

register_fetcher(my_internal_db_fetcher, priority=1.0, name="internal_db")

Register before the first call to run / screen_by_* (e.g. at module import time). Registered fetchers are tried before the built-in chain in priority order; the first one to return a non-empty FetchResult.text wins. Exceptions inside a fetcher are logged to stderr and the next fetcher is tried.

Validation

An independent evaluation of the GEO screening and metadata extraction workflow is available at rachadele/biolit-eval. It uses a bootstrap resampling pipeline to estimate precision, recall, and F1 against a manually curated ground truth of 509 GEO accessions labelled for transcription factor perturbation experiments.

Known Limitations

• Papers without abstracts or accessible full text are skipped silently.
• GEO records attempt full-text retrieval via linked PMIDs. text_source will be geo_linked_fulltext, geo_linked_abstract, or geo_record depending on what was accessible.
• bioRxiv/medRxiv JATS XML is frequently blocked by Cloudflare regardless of headers. The pipeline falls back to the title and abstract from the bioRxiv API (text_source: preprint_abstract).
• The Semantic Scholar API allows roughly 100 unauthenticated requests per day. Set SEMANTIC_SCHOLAR_API_KEY in .env for higher limits.