pubmed-stream MCP server

pubmed-stream: literature searches in PubMed, for researchers and their AI assistants

<!-- mcp-name: io.github.melhzy/pubmed-stream -->

From a PubMed search to a knowledge graph of the literature. pubmed-stream downloads full-text articles from PubMed Central, maps the evidence around key papers, and links everything a study collects into an Obsidian knowledge graph, where papers connect through citations, similarity and MeSH topics. It is built on NCBI's official E-utilities, works from the command line or Python, and needs nothing beyond requests. AI assistants use it too: it is a plugin for Claude Code and Codex, and an MCP server for them and Gemini CLI, so you can ask a research question in plain English and get an answer that cites only papers the assistant downloaded and read.

Documentation: melhzy.github.io/pubmed-stream, the tutorials, guides and reference in one searchable website, with every notebook shown with its results.

A study made with it: the Aβ\56 retraction impact page**, how a retracted Alzheimer's paper spread through the literature and how much of it language models learned, as an interactive page where every number comes from the study's files; see Studies.

New to programming? Start with tutorial 0, which takes a research question to a map of its literature in an afternoon. Click Open in Colab to run it in your web browser, with nothing to install; you only type your question into one settings cell.

flowchart LR
    S["PubMed searches"] -->|"download"| D["Full-text articles<br/>metadata, references, text"]
    K["Key papers"] -->|"neighborhood"| N["Similar, citing and cited papers<br/>flagged reviews, retractions, NIH funding"]
    D --> G["Knowledge graph<br/>kg_topic, an Obsidian vault"]
    N --> G
    G --> O["Explore, write, refresh"]

Contents

• Quick start
• Tutorials
• Studies
• Installation and configuration
• 1. Download full-text articles
• 2. Write searches that find the right papers
• 3. Map the evidence around a paper
• 4. Build the knowledge graph of a study
• 5. Use it with AI assistants (MCP and plugin), local models and Obsidian
• Python API
• Troubleshooting
• How it works

Quick start

pip install pubmed-stream

# Size a search and preview its newest titles, without downloading: it counts PMC and all of PubMed
pubmed-stream search '"gut microbiota"[Title] AND frailty[Title/Abstract]'

# Download the full text of up to 50 articles, add the abstracts of those whose full text is not

Shortened. The full README is on GitHub.