Biolit MCP

BioLit MCP

Citation-grounded biomedical literature review for Claude. An MCP server over PubMed, Europe PMC, NCBI Gene and UniProt, an Evidence Review agent skill, and a 50-question benchmark that measures whether answers can be trusted.

Scientists don't adopt AI research assistants that invent citations. BioLit is built around one rule: Claude may only cite papers it has actually retrieved and read in the session, and the benchmark checks that every cited PMID exists and supports its claim.

flowchart LR
    U[Scientist] --> C[Claude + Evidence Review skill]
    C -- MCP --> S[BioLit server]
    S --> P[PubMed E-utilities<br/>search]
    S --> E[Europe PMC<br/>abstracts + citation check]
    S --> G[NCBI Gene]
    S --> UP[UniProt Swiss-Prot]
    C --> A[Answer: claims → PMIDs]
    A --> B[Benchmark: PMID exists? claim supported?]

Tools

All tools are read-only. Production details: request timeouts, retry with exponential backoff on 429/5xx, NCBI rate limiting (3 req/s, or 10 req/s with NCBI_API_KEY), input validation, and error messages that are safe to show the model.

Quick start

Requires Python 3.11+ and uv. Claude Code

claude mcp add biolit -- uvx --from git+https://github.com/sanikacentric/biolit-mcp biolit-mcp
mkdir -p ~/.claude/skills && cp -r skills/evidence-review ~/.claude/skills/

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "biolit": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/sanikacentric/biolit-mcp", "biolit-mcp"],
      "env": { "NCBI_EMAIL": "you@example.com" }
    }
  }
}

Remote (Streamable HTTP): biolit-mcp --transport streamable-http

Optional environment variables: NCBI_API_KEY (higher rate limit), NCBI_EMAIL (recommended by NCBI).

Then ask: "What is the evidence that lecanemab slows cognitive decline in early Alzheimer's disease?"

Evidence Review skill

skills/evidence-review/SKILL.md gives Claude a scientist's workflow: resolve entities → run focused searches → read abstracts → write one claim per sentence with PMIDs → self-check every citation before answering. The output separates the bottom line, evidence, strength & gaps, and references.

Benchmark

evals/ runs 50 questions with and without the BioLit tools:

• 30 fact questions: UniProt accession + chromosome for well-characterized human genes, scored exactly.
• 20 literature questions: across oncology, cardiometabolic, neuro, gene therapy and structural biology. Every cited PMID is checked live against PubMed, and an LLM judge decides whether the cited abstracts support each claim.

pip install -e ".[eval]"
export ANTHROPIC_API_KEY=...
python evals/run_eval.py --mode both              # full run (~100 agent runs)
python evals/run_eval.py --mode tools --limit 3   # smoke test

Results

Results go here after the first full run (evals/results/*.json).

Development

pip install -e ".[dev,eval]"
pytest          # unit tests with recorded API responses; no network or API key needed
ruff check . && ruff format --check .

Limitations

• Evidence is read from abstracts, not full text.
• The claim-support judge is an LLM. Spot-check its verdicts before relying on the numbers.
• Not for clinical decision-making.

License

MIT