mcp-pubmed-evidence

MCP server for reliable PubMed literature retrieval, BibTeX export, and evidence table generation for biomedical research agents.

This project exposes PubMed as structured MCP tools so AI assistants can retrieve biomedical literature with source URLs, PMID/DOI metadata, article types, and citation-ready outputs instead of relying on model memory.

Status

Early development. The first version focuses on PubMed metadata retrieval and citation provenance.

Features

• Search PubMed with optional year and publication type filters
• Search ClinicalTrials.gov by query, condition, intervention, status, and result limit
• Report result-limit metadata, including requested limits, effective limits, returned counts, and truncation flags
• Summarize source provenance at the response level so agents can see which databases contributed results
• Validate biomedical research queries and reject obvious personal medical advice requests
• Optionally write local JSONL audit logs for MCP tool calls without storing full query text or abstracts
• Fetch normalized metadata for a PubMed article by PMID
• Export PubMed records as BibTeX entries
• Build compact evidence tables for agent workflows
• Return structured PubMed fields such as PMID, title, authors, journal, year, DOI, abstract, article types, and PubMed URL
• Return structured trial fields such as NCT ID, condition, intervention, phase, status, enrollment, outcomes, locations, sponsors, and linked publication references

Why MCP for Biomedical Evidence

Biomedical research agents need reliable access to current, source-backed evidence. A plain chatbot can answer from model memory, but it may miss recent papers, blur study types, or provide weak citations. This MCP server gives agents a controlled tool layer for PubMed retrieval, ClinicalTrials.gov trial registry retrieval, structured metadata, citation export, and evidence-table generation.

The goal is not to make medical decisions. The goal is to help agents retrieve and organize biomedical literature with provenance, stable schemas, and clear source URLs.

Safety scope

This server is intended for biomedical research support, literature discovery, citation management, and evidence organization. It is not intended for diagnosis, treatment recommendations, or medical advice.

Tools reject obvious personal medical advice prompts such as requests to diagnose the user, prescribe medication, or decide whether the user should take or stop a treatment. Research-oriented terms such as diagnosis, treatment, and therapy remain valid when used for literature or trial retrieval.

Installation

git clone https://github.com/Tianyu-Qu/mcp-pubmed-evidence.git
cd mcp-pubmed-evidence
python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -e .[dev]

For macOS/Linux, activate the virtual environment with:

source .venv/bin/activate

MCP Configuration

This server uses MCP stdio transport and can be used by any MCP-compatible client.

Generic stdio configuration:

{
  "command": "python",
  "args": ["-m", "mcp_pubmed_evidence.server"],
  "env": {
    "PYTHONPATH": "/path/to/mcp-pubmed-evidence/src"
  }
}

Claude Desktop example:

{
  "mcpServers": {
    "pubmed-evidence": {
      "command": "python",
      "args": ["-m", "mcp_pubmed_evidence.server"],
      "env": {
        "PYTHONPATH": "/path/to/mcp-pubmed-evidence/src"
      }
    }
  }
}

Replace /path/to/mcp-pubmed-evidence/src with the absolute path to your local src directory. On Windows, it may look like C:\\path\\to\\mcp-pubmed-evidence\\src.

If you install the project into the same Python environment used by your MCP client, you can omit PYTHONPATH.

If your network requires a proxy, add HTTP_PROXY and HTTPS_PROXY to env:

"HTTP_PROXY": "http://127.0.0.1:7890",
"HTTPS_PROXY": "http://127.0.0.1:7890"

Local Demo

You can test PubMed retrieval without an MCP client:

python examples/search_pubmed.py "Alzheimer disease machine learning" --max-results 3

With a year filter:

python examples/search_pubmed.py "Alzheimer disease machine learning" --max-results 5 --year-from 2022 --year-to 2026

Search ClinicalTrials.gov without an MCP client:

python examples/search_trials.py --condition "Alzheimer disease" --intervention "GLP-1" --max-results 5

Build a unified PubMed + ClinicalTrials.gov evidence table without an MCP client:

python examples/build_biomedical_evidence_table.py --query "Alzheimer disease machine learning" --condition "Alzheimer disease" --max-pubmed-results 2 --max-trial-results 2

To print the same rows as JSON:

python examples/build_biomedical_evidence_table.py --query "Alzheimer disease machine learning" --condition "Alzheimer disease" --max-pubmed-results 2 --max-trial-results 2 --json

Example outputs are available in examples/sample_search_output.json, examples/sample_trial_output.json, and examples/sample_biomedical_evidence_table.json.

You can also verify the MCP stdio server locally by listing its tools:

python examples/mcp_list_tools.py

Expected tools:

search_pubmed
get_pubmed_article
get_abstract
export_bibtex
build_evidence_table
search_trials
get_trial_summary
map_trial_to_publications
build_biomedical_evidence_table

Demo

Verified with Cursor as an MCP client. Cursor connected to the pubmed-evidence server and called the search_pubmed tool for the query Alzheimer disease machine learning with max_results=3.

Additional result screenshots:

Tools

search_pubmed

Search PubMed and return normalized article metadata.

Search responses include metadata with source_name, source_url, query_summary, requested_max_results, effective_max_results, max_allowed_results, returned_count, total_available when available, and truncated.

Inputs:

• query: PubMed search query
• max_results: maximum number of articles to return, capped at 50
• year_from: optional publication year lower bound
• year_to: optional publication year upper bound
• article_types: optional publication type filters, such as Review or Randomized Controlled Trial

get_pubmed_article

Fetch one PubMed article by PMID.

export_bibtex

Fetch PubMed articles by PMID and export BibTeX entries.

build_evidence_table

Fetch PubMed articles by PMID and return compact evidence table rows.

search_trials

Search ClinicalTrials.gov and return compact trial records.

Search responses include metadata with source provenance, query summary, result-limit, and truncation information.

Inputs:

• query: optional general trial query
• condition: optional condition or disease filter
• intervention: optional intervention, drug, or device filter
• status: optional recruitment status filter
• max_results: maximum number of trials to return, capped at 50

get_trial_summary

Fetch one ClinicalTrials.gov trial by NCT ID and return detailed structured metadata including arms, outcomes, eligibility, locations, sponsors, collaborators, references, and result references.

map_trial_to_publications

Map one ClinicalTrials.gov NCT ID to linked PubMed publications when PMIDs are available in ClinicalTrials.gov references.

build_biomedical_evidence_table

Build a unified biomedical evidence table from PubMed articles and ClinicalTrials.gov trial records.

Inputs:

• query: optional PubMed query and optional trial query
• condition: optional condition or disease filter for ClinicalTrials.gov
• intervention: optional intervention, drug, or device filter for ClinicalTrials.gov
• max_pubmed_results: maximum PubMed records to include
• max_trial_results: maximum ClinicalTrials.gov records to include

Returns metadata plus integrated evidence rows. Rows include source type, source ID, title, date/year, study type, status, phase, conditions, interventions, outcomes, DOI, URL, and provenance.

The metadata includes query summary, sources used, source counts, requested/effective PubMed and ClinicalTrials.gov result limits, maximum allowed limits, returned row count, and whether a requested limit was truncated.

Development

Run tests:

pytest

Run linting:

ruff check .

Audit Logging

Audit logging is disabled by default. To enable local JSONL audit logs for MCP tool calls, set MCP_PUBMED_EVIDENCE_AUDIT_LOG to a file path before starting the MCP server:

$env:MCP_PUBMED_EVIDENCE_AUDIT_LOG = "F:\Healthcare\mcp-pubmed-evidence\audit.jsonl"

Audit events include timestamp, tool name, status, sanitized argument summaries, result counts, truncation flags, and source counts when available. They intentionally do not store full query text, abstracts, eligibility criteria, or other long biomedical text fields.

Limitations

• PubMed and ClinicalTrials.gov metadata can be incomplete; DOI, abstract, author, journal, publication date, outcomes, locations, or linked PMIDs may be missing.
• Evidence tables are metadata-oriented in the first version and do not extract PICO elements or judge study quality.
• Result limits are capped to keep MCP responses manageable; tools report truncation metadata when a request exceeds the configured limit or when a source reports more available records than returned.
• Query validation is a lightweight safety guardrail, not a complete medical-intent classifier.
• Audit logging is local and opt-in; users are responsible for choosing an appropriate log path and retention policy.
• The server does not provide diagnosis, treatment recommendations, or medical advice.
• Network access to PubMed may require a proxy depending on the user's environment.
• Tool outputs should be reviewed by a human before being used in manuscripts, clinical documents, or systematic reviews.

Release Notes

See CHANGELOG.md for v0.1.0 release notes.

v0.3.0 Development

The next milestone, v0.3.0 Evidence Table 2.0, introduces a unified biomedical evidence row schema for combining PubMed articles and ClinicalTrials.gov trial records. The build_biomedical_evidence_table MCP tool now returns integrated evidence rows with source provenance.

v0.4.0 Development

The v0.4.0 Evidence Quality & Safety milestone adds guardrails for agent-facing biomedical tools. Current improvements add explicit result-limit, truncation, query-summary, source-provenance metadata, lightweight biomedical query validation, and opt-in local audit logging so MCP clients can tell where evidence came from, whether a response was capped, and whether a request falls outside the research-support scope.

Roadmap

• Expand ClinicalTrials.gov result fields and examples
• Add OpenAlex/Crossref DOI resolution
• Add richer evidence table extraction
• Add example MCP client configurations
• Add local PDF library support