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

Related MCP server: Academic Paper Search MCP Server

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:

1. Start from a topic, DOI, or author.

2. Inspect papers.

3. Read abstracts or full text.

4. Expand references.

5. Jump to related authors.

6. Save promising papers.

7. 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:

1. Search papers

2. Resolve authors

3. Expand to author papers

4. Read abstracts / full text

5. Expand references

6. Save promising papers into a named in-memory reading list

7. 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.