ScholarFetch

Name: ScholarFetch
Author: laibniz

by laibnizversion 0.2.1i★ –iRuns in the cloud

Registry nameio.github.laibniz/scholarfetch

Install

Overview

Description from the maker (original English)

Multi-engine scholarly research server for search, traversal, full text, and reading lists.

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 in the cloud
Transport: Streamable HTTP
Category: Search & AI memory

Install

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

Before you install

Basic checks: 1 of 1 passed
Runs on the maker’s server: what you ask it goes through their server.
The registry lists no key to fill in.

You need

Runs in the cloudNothing to install; it connects over the internet

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

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

Works in Claude on the web, desktop and mobile:

Open Settings → Connectors
Press Add custom connector
Paste the address below and press Add
If it asks you to sign in, sign in with the maker

https://laibniz-scholarfetch-web.hf.space/mcp/

Run this in your terminal

claude mcp add --transport http scholarfetch https://laibniz-scholarfetch-web.hf.space/mcp/

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "scholarfetch": {
      "url": "https://laibniz-scholarfetch-web.hf.space/mcp/"
    }
  }
}

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": {
    "scholarfetch": {
      "type": "http",
      "url": "https://laibniz-scholarfetch-web.hf.space/mcp/"
    }
  }
}

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.

ChatGPT connects only to servers on the internet:

Open ChatGPT on the web → Settings → Connectors (Apps)
Press Create or Add (some plans need Developer mode under Advanced first)
Paste the address below, give it a name and save
If it asks you to sign in, sign in with the maker

https://laibniz-scholarfetch-web.hf.space/mcp/

Menu names can differ slightly by plan and over time.

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

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

What it can do

We have not read this server’s tool list yet; we check endpoints in rotation.

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 laibniz, 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 checked yet
Endpoint responds
Not checked yet; we check endpoints in rotation
… Not checked yet
Sign-in with OAuth
Not checked yet; we check endpoints in rotation
… Not checked yet
No hidden instructions in tool descriptions
Not checked yet; we read the tool list when we check the endpoint

Compared with others in Search & AI memoryi

More stars than 0% of the other 7258 servers in this category
Published to the registry more recently than 6% of the other 7258 servers in this category
Basic checks equal to or better than 100% of the other 7258 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 ScholarFetch 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?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs in the maker’s cloud. Connect over the internet with nothing to install, but what you send passes through the maker’s servers.

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.2.1) reached the registry 8 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

Install

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.

ScholarFetch

ScholarFetch is a multi-engine academic research environment for:

terminal-first literature exploration
MCP-powered agent workflows
building curated reading lists and exportable research corpora

It combines:

a rich interactive CLI for humans
a classic MCP server (stdio)
a FastMCP server (stdio, sse, streamable-http)

The core idea is simple: start from keywords, DOI, or authors, traverse papers and references, inspect abstracts and full text, save what matters, then export a compact corpus for synthesis.

What ScholarFetch Does

Searches across multiple scholarly engines in parallel
Resolves ambiguous author identities and expands author paper lists
Traverses references as first-class research nodes
Retrieves abstracts and machine-readable full text when available
Tracks a saved paper set during an interactive research session
Exports citations, abstracts, BibTeX, or full-text corpora
Exposes the same research workflow to MCP agents
Maintains stateful saved-paper collections inside one MCP session

Engines

Elsevier (Scopus / Abstract / Article retrieval)
OpenAlex
Crossref
arXiv
Europe PMC
Springer Nature (metadata + open access)
Semantic Scholar (DOI enrichment path)

Installation

git clone https://github.com/laibniz/scholarfetch.git

cd scholarfetch python3 -m venv .venv source .venv/bin/activate pip install -e . scholarfetch

Console scripts:

scholarfetch
scholarfetch-mcp
scholarfetch-fastmcp

Alternative:

python3 scholarfetch.py

Credentials

ScholarFetch loads provider credentials server-side / client-side from environment.

Default env file:

.scholarfetch.env

Typical variables:

ELSEVIER_API_KEY=...

ELSEVIER_INSTTOKEN=... SPRINGER_META_API_KEY=... SPRINGER_OPENACCESS_API_KEY=...

Notes:

ELSEVIER_INSTTOKEN is optional
provider entitlements and rate limits still apply
MCP tools do not accept API keys in tool arguments

CLI Research Workflow

ScholarFetch CLI is designed for research traversal.

Typical flow:

Start from a topic, DOI, or author.
Inspect papers.
Read abstracts or full text.
Expand references.
Jump to related authors.
Save promising papers.
Export a corpus for downstream work.

Example:

/search graph neural networks

/author Albert Einstein /papers 1 has:abstract /article 1 /refs 1 /saved /export fulltext dummy corpus.txt

CLI Features

Interactive picker with tree navigation
Breadcrumbs for current research position
Action bar for OPEN, ABSTRACT, TEXT, REFS, and AUTHOR
Backspace to go to parent node
Esc to return to prompt
S to save a paper from paper lists or reference lists
X to remove from the saved list
AUTHOR action from a paper now lets you select:
a single author
ALL AUTHORS
Reference lists behave like paper lists:
open
abstract
text
refs
author
Automatic paper availability hints:
abstract availability
full-text availability
Progress feedback for expensive transitions
Interruptible reference preview building with partial results kept

Core CLI Commands

/search
/author
/papers [filters]
/doi
/open
/abstract
/article
/refs
/ref
/saved
/export [format style path ...]
/import [path]
/pick [mode]
/config
/engines
/help

Paper Filters

Use with /papers:

year>=YYYY, year<=YYYY, year=YYYY
has:abstract, has:doi, has:pdf, has:fulltext
venue: , title: , doi:

Examples:

/papers 1 year>=2020 has:abstract

/papers 1 has:fulltext /papers andrea de mauro venue:marketing

Export Modes

ScholarFetch supports four export modes from the saved paper set.

bib
BibTeX for citation managers and bibliographic tooling
citations
citation-only export in harvard, apa, or ieee
abstracts
metadata + abstract for each saved paper
fulltext
metadata + abstract + full text when available
optional inclusion of references

This makes ScholarFetch useful as a corpus builder for downstream synthesis agents.

MCP Server

ScholarFetch exposes the same research model through MCP.

Modes:

Classic MCP (stdio): python3 scholarfetch_mcp.py
FastMCP stdio: python3 scholarfetch_fastmcp.py --transport stdio
FastMCP SSE: python3 scholarfetch_fastmcp.py --transport sse --host 127.0.0.1 --port 8000
FastMCP streamable HTTP: python3 scholarfetch_fastmcp.py --transport streamable-http --host 127.0.0.1 --port 8000 --http-path /mcp

Validation:

python3 scholarfetch_mcp.py --self-test

python3 scholarfetch_fastmcp.py --self-test

Public demo endpoints:

Web UI: https://huggingface.co/spaces/Laibniz/ScholarFetch_Web
Public MCP endpoint: https://laibniz-scholarfetch-web.hf.space/mcp/
MCP Registry listing: io.github.laibniz/scholarfetch

MCP Research Model

The MCP server is designed for agent workflows, not only one-off calls.

An agent can:

Search papers
Resolve authors
Expand to author papers
Read abstracts / full text
Expand references
Save promising papers into a named in-memory reading list
Export the reading list as:

citations
abstracts
BibTeX
full-text corpus

This lets an agent build a focused research set inside one MCP session and then hand off an export artifact to another synthesis step.

See MCP_SERVER.md for the detailed tool model.

Repository Files

scholarfetch.py: CLI entrypoint
scholarfetch_cli.py: core CLI + retrieval logic
scholarfetch_mcp.py: classic MCP server
scholarfetch_fastmcp.py: FastMCP server
MCP_SERVER.md: MCP usage guide
AGENTS.md: agent-facing workflow guide
SKILL.md: structured research skill guide
SKILLS.md: index for agent-facing skill docs
CONTRIBUTING.md: contributor notes

For Agents

If you are running ScholarFetch from an MCP-compatible system, read:

AGENTS.md
SKILL.md
SKILLS.md

These documents explain how to use ScholarFetch as a literature-research environment rather than as a flat search API.

Contributing

See CONTRIBUTING.md.

Security

See SECURITY.md.

License

MIT License. See LICENSE.

ScholarFetch

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

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 Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

ScholarFetch

What ScholarFetch Does

Engines

Installation

Credentials

CLI Research Workflow

CLI Features

Core CLI Commands

Paper Filters

Export Modes

MCP Server

MCP Research Model

Repository Files

For Agents

Contributing

Security

License