pubmed-stream

Name: pubmed-stream
Author: melhzy

by melhzyversion 0.4.4i★ –iRuns on your computer

Registry nameio.github.melhzy/pubmed-stream

Install

Overview

Description from the maker (original English)

Search PubMed and PMC, read full texts, map evidence and build Obsidian knowledge graphs

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Clinics & health
You need to fill in: NCBI_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: NCBI_API_KEY, NCBI_EMAIL

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "pubmed-stream": {
      "command": "uvx",
      "args": [
        "pubmed-stream",
        "mcp"
      ],
      "env": {
        "NCBI_API_KEY": "your-value",
        "NCBI_EMAIL": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add pubmed-stream --env NCBI_API_KEY=your-value --env NCBI_EMAIL=your-value -- uvx pubmed-stream mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "pubmed-stream": {
      "command": "uvx",
      "args": [
        "pubmed-stream",
        "mcp"
      ],
      "env": {
        "NCBI_API_KEY": "your-value",
        "NCBI_EMAIL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "pubmed-stream": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "pubmed-stream",
        "mcp"
      ],
      "env": {
        "NCBI_API_KEY": "your-value",
        "NCBI_EMAIL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

NCBI_API_KEY secret env
Optional free NCBI API key, which raises NCBI's limit from 3 to 10 requests a second; create it under API Key Management at https://account.ncbi.nlm.nih.gov/settings/
NCBI_EMAIL env
Optional contact email that NCBI asks programs to send with their requests

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account melhzy, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Clinics & healthi

More stars than 0% of the other 871 servers in this category
Published to the registry more recently than 66% of the other 871 servers in this category
Basic checks equal to or better than 100% of the other 871 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is pubmed-stream safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: NCBI_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.4.4) reached the registry 15 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Mirobody, FHIR MCP Server, Google Health MCP

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

pubmed-stream: PubMed search and PubMed Central full-text download, for researchers and their AI assistants

pubmed-stream is an open-source Python library and command-line tool that takes a PubMed search to a knowledge graph of the literature. It is built on NCBI's official E-utilities API, 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 a PubMed 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.

With it you can:

Search PubMed and PubMed Central (PMC) with field tags and MeSH (Medical Subject Headings), and see how many papers a search finds before downloading anything (section 2)
Bulk-download full-text articles from PubMed Central as JSON, with metadata, references and text or JATS XML, and the PubMed abstracts of articles without free full text (section 1)
Export citations as BibTeX or RIS for Zotero, EndNote and Mendeley (section 1)
Find the papers that cite a paper, the papers it cites and the papers PubMed rates as similar, with retracted papers, reviews and NIH funding flagged (section 3)
Build a knowledge graph of a study's literature in Obsidian, one Markdown note per paper and MeSH concept, linked by citations, similarity and topics (section 4)
Give AI assistants PubMed tools through the MCP server or the plugin, and summarize abstracts with a local LLM through Ollama, so nothing leaves your computer (section 5)
Measure the impact of a retracted paper through the papers citing it and the papers citing those (tutorial 8)

Documentation: [melhzy.github.io/pubmed-stream](https://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 metadata, references, text"] K["Key papers"] -->|"neighborhood"| N["Similar, citing and cited papers flagged reviews, retractions, NIH funding"] D --> G["Knowledge graph kg_topic, an Obsidian vault"] N --> G G --> O["Explore, write, refresh"]

Quick start
Tutorials
Studies
Installation and configuration
1. Download full-text articles from PubMed Central
2. Write PubMed searches that find the right papers
3. Map the evidence around a paper: citing, cited and similar articles
4. Build the knowledge graph of a study in Obsidian
5. Use it with AI assistants (MCP server and plugin), local models and Obsidian
Python API for PubMed and NCBI E-utilities
Troubleshooting
How it works
Contributing
FAQ
Citation, acknowledgments and license

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

free, then read them compactly

pubmed-stream download '"gut microbiota"[Title] AND frailty[Title/Abstract]' --max-results 50 pubmed-stream abstracts '"gut microbiota"[Title] AND frailty[Title/Abstract]' --max-results 200 pubmed-stream list publications --abstracts

Export them as citations for Zotero, EndNote or Mendeley

pubmed-stream export publications -o frailty.bib

Map the evidence around one paper: similar, citing and cited papers, with retractions flagged

pubmed-stream neighborhood 31319564

Build a study's knowledge graph in ./kg_gut_microbiome_frailty

pubmed-stream kg "gut microbiome frailty" -q '"gut microbiota"[Title] AND frailty[Title/Abstract]' --seed 31319564

First add the folder in Obsidian with Open folder as vault, or use the automatic setup below.

After that, open the graph's home note from the terminal:

pubmed-stream open kg_gut_microbiome_frailty

Summarize the 10 newest abstracts and save the summaries in the knowledge graph. --model picks who

summarizes: a model on your own computer, in Ollama (https://ollama.com), so nothing leaves it, or the

model of an AI assistant installed below: claude, codex or gemini. Section 5 helps you choose

ollama pull qwen3.5:4b pubmed-stream review "Is the gut microbiota linked to frailty?" --limit 10 --model qwen3.5:4b --vault kg_gut_microbiome_frailty pubmed-stream review "Is the gut microbiota linked to frailty?" --limit 10 --model claude --vault kg_gut_microbiome_frailty

Or ask in plain English and let Claude Code, Codex or Gemini CLI run all of this: see below

For automatic first-time Obsidian setup with --register, see Open the graph in Obsidian. That section also explains how to use the updated source if your installed command does not yet support the option.

With an AI assistant: Claude Code, Codex or Gemini CLI

An AI assistant can do all of the above for you. Install one: Claude Code needs a paid Claude plan, Codex a paid ChatGPT plan, and Gemini CLI only a Google account. These commands need Node.js 22 or later; tutorial 7 shows the other installers and walks through a session.

npm install -g @anthropic-ai/claude-code   # Claude Code

npm install -g @openai/codex # Codex npm install -g @google/gemini-cli # Gemini CLI

Then give your assistant pubmed-stream's tools, in one of two ways:

# Claude Code or Codex: install the plugin once, for every folder. It needs uv (https://docs.astral.sh/uv/)

claude plugin install pubmed-stream --marketplace melhzy/pubmed-stream codex plugin marketplace add melhzy/pubmed-stream codex plugin add pubmed-stream@pubmed-stream

Any of the three: set up one research folder with the MCP server and instructions

mkdir gut_frailty cd gut_frailty pubmed-stream init

Start your assistant in your research folder with claude, codex or gemini. The first time, it asks you to sign in. With init, Claude Code also asks to approve the pubmed-stream server, and Gemini CLI asks whether you trust the folder: say yes to both, or the assistant will not have pubmed-stream's tools. /mcp lists them. Then ask in plain language:

*Is the gut microbiota linked to frailty? Size the search first, download the clinical studies, and summarize the evidence with PMIDs.*

Section 5 compares the plugin and init.

In Python

The first steps of the quick start, in Python:

from pubmed_stream import create_kg, get_neighborhood, search_and_download

stats = search_and_download('"gut microbiota"[Title] AND frailty[Title/Abstract]', max_results=50) hood = get_neighborhood("31319564") result = create_kg("gut microbiome frailty", queries=['"gut microbiota"[Title] AND frailty[Title/Abstract]'], seeds=["31319564"]) print(stats.successful, hood.totals, result.kg_dir)

Tutorials

The notebooks in tutorials/ are written for researchers, not programmers. Each follows a real research question. The notebooks have a single Your settings cell for your own question, run in Google Colab with nothing to install, and are saved with their results so you can read them first, on GitHub or on the documentation site; tutorial 7 happens in a terminal, with an AI assistant.

#	Tutorial	Use case
0	Start here: a new research topic in an afternoon	From a question to a map of its literature: size the field, find the paper everyone cites, build a knowledge graph, read its themes, evidence and gaps, export a reading list
1	Getting started	Download a search, read the files, choose a format, resume, fetch one known article, rescue a search that finds nothing, read downloads compactly
2	Designing searches	Measure the size, precision and availability of five versions of one search, and combine words with MeSH headings
3	A literature-review corpus	Run a keyword list, deduplicate, screen by theme, export to CSV and BibTeX
4	The evidence around one paper	Map a paper's neighborhood and screen it for retractions
5	The knowledge graph of a study	Build a graph and explain it with twelve charts and tables
6	Text mining and RAG preparation	Split articles into sections and chunk them for embeddings

| 7 | Your literature search with an AI assistant | For medical students and biologists: install Claude Code or Codex, connect it to pubmed-stream's MCP tools with the plugin or pubmed-stream init, choose a model, ask in plain English, check the evidence, keep it in Obsidian,

Only the beginning is shown; read the rest in the repo

pubmed-stream

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Clinics & healthi

Common questions

Alternatives

Mirobody

FHIR MCP Server

Google Health MCP

README from the maker

pubmed-stream: PubMed search and PubMed Central full-text download, for researchers and their AI assistants

Contents

Quick start

Size a search and preview its newest titles, without downloading: it counts PMC and all of PubMed

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

free, then read them compactly

Export them as citations for Zotero, EndNote or Mendeley

Map the evidence around one paper: similar, citing and cited papers, with retractions flagged

Build a study's knowledge graph in ./kg_gut_microbiome_frailty

First add the folder in Obsidian with Open folder as vault, or use the automatic setup below.

After that, open the graph's home note from the terminal:

Summarize the 10 newest abstracts and save the summaries in the knowledge graph. --model picks who

summarizes: a model on your own computer, in Ollama (https://ollama.com), so nothing leaves it, or the

model of an AI assistant installed below: claude, codex or gemini. Section 5 helps you choose

Or ask in plain English and let Claude Code, Codex or Gemini CLI run all of this: see below

With an AI assistant: Claude Code, Codex or Gemini CLI

Any of the three: set up one research folder with the MCP server and instructions

In Python

Tutorials