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 <keywords|doi|person name>
/author <name>
/papers <author name|index> [filters]
/doi <doi>
/open <index>
/abstract <doi|index>
/article <doi|index>
/refs <doi|index>
/ref <index>
/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:<text>, title:<text>, doi:<text>

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:

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.