io.github.laibnizPublished by GitHub account laibnizv0.2.1Updated Oct 27, 2026
Multi-engine scholarly research server for search, traversal, full text, and reading lists.
Multi-engine scholarly research server that lets an assistant search papers, traverse authors and references, read abstracts and full text, and export reading…
Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.
Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.
Read https://sourceweft.com/mcp/io.github.laibniz%2Fscholarfetch/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.Add this to your client's mcpServers config.
{
"mcpServers": {
"scholarfetch": {
"type": "http",
"url": "https://laibniz-scholarfetch-web.hf.space/mcp/"
}
}
}ScholarFetch is a multi-engine academic research environment for:
It combines:
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.
Console scripts:
scholarfetchscholarfetch-mcpscholarfetch-fastmcpAlternative:
ScholarFetch loads provider credentials server-side / client-side from environment.
Default env file:
.scholarfetch.envTypical variables:
Notes:
ELSEVIER_INSTTOKEN is optionalScholarFetch CLI is designed for research traversal.
Typical flow:
Example:
OPEN, ABSTRACT, TEXT, REFS, and AUTHORBackspace to go to parent nodeEsc to return to promptS to save a paper from paper lists or reference listsX to remove from the saved listAUTHOR action from a paper now lets you select:
ALL AUTHORSopenabstracttextrefsauthor/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/helpUse with /papers:
year>=YYYY, year<=YYYY, year=YYYYhas:abstract, has:doi, has:pdf, has:fulltextvenue:<text>, title:<text>, doi:<text>Examples:
ScholarFetch supports four export modes from the saved paper set.
bib
citations
harvard, apa, or ieeeabstracts
fulltext
This makes ScholarFetch useful as a corpus builder for downstream synthesis agents.
ScholarFetch exposes the same research model through MCP.
Modes:
python3 scholarfetch_mcp.pypython3 scholarfetch_fastmcp.py --transport stdiopython3 scholarfetch_fastmcp.py --transport sse --host 127.0.0.1 --port 8000python3 scholarfetch_fastmcp.py --transport streamable-http --host 127.0.0.1 --port 8000 --http-path /mcpValidation:
Public demo endpoints:
io.github.laibniz/scholarfetchThe MCP server is designed for agent workflows, not only one-off calls.
An agent can:
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.
scholarfetch.py: CLI entrypointscholarfetch_cli.py: core CLI + retrieval logicscholarfetch_mcp.py: classic MCP serverscholarfetch_fastmcp.py: FastMCP serverMCP_SERVER.md: MCP usage guideAGENTS.md: agent-facing workflow guideSKILL.md: structured research skill guideSKILLS.md: index for agent-facing skill docsCONTRIBUTING.md: contributor notesIf 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.
See CONTRIBUTING.md.
See SECURITY.md.
MIT License. See LICENSE.
Source: README.md at commit 103fcd5
Start a research traversal from keywords, a DOI, or a person name. Returns deduplicated paper records that you can inspect, save, expand through references, or use as seeds for author exploration. If you pass `engines`, use a comma-separated subset of: elsevier, openalex, crossref, arxiv, europepmc, springer, semanticscholar.
limitqueryrequiredenginesEnrich one known DOI with metadata, reading links, and full-text availability signals. If you pass `engines`, use a comma-separated subset of: elsevier, openalex, crossref, arxiv, europepmc, springer, semanticscholar.
doirequiredenginesDisambiguate a human author name into ranked identity candidates. Use this before `scholarfetch_author_papers` when the name is ambiguous and you need a stable `candidate_index`. If you pass `engines`, it must include `openalex`.
namerequiredlimitenginesExpand one author into a deduplicated paper list. This is the main author->paper traversal tool and supports research filters. Use `author_id` when you already know the exact author, or `author_name` plus `candidate_index` after `scholarfetch_author_candidates`. Supported comma-separated `filters`: year>=YYYY, year<=YYYY, year=YYYY, has:abstract, has:doi, has:pdf, venue:<text>, title:<text>, doi:<text>. If you pass `engines`, it must include `openalex`.
limitenginesfiltersauthor_idauthor_namecandidate_indexRead the best abstract available for a paper. Use with a DOI or with author_name + candidate_index + paper_index after author_papers. If you pass `engines`, use a comma-separated subset of: elsevier, openalex, crossref, arxiv, europepmc, springer, semanticscholar.
doienginesauthor_namepaper_indexcandidate_indexRead full paper text when machine-readable content is recoverable. Use with a DOI or with author_name + candidate_index + paper_index. Uses Elsevier first, then open-access fallbacks such as Springer OA, Europe PMC, arXiv PDF, and generic PDF URLs when text is recoverable. If you pass `engines`, use a comma-separated subset of: elsevier, openalex, crossref, arxiv, europepmc, springer, semanticscholar.
doienginesauthor_namepaper_indexcandidate_indexExpand a paper into its references. Use with a DOI or with author_name + candidate_index + paper_index. This is the main edge-expansion tool for traversing the literature graph. If you pass `engines`, use a comma-separated subset of: elsevier, openalex, crossref, arxiv, europepmc, springer, semanticscholar.
doienginesauthor_namepaper_indexcandidate_indexAdd one paper to a named in-memory reading list on the MCP server. Best input is paper_json copied from another ScholarFetch tool result, but DOI, query+result_index, or author_name+candidate_index+paper_index also work. Reuse the same collection name across calls to keep one research session together.
doiqueryenginescollectionpaper_jsonauthor_namepaper_indexresult_indexcandidate_indexList all papers currently saved in a named in-memory reading list. Use this to inspect the working set before exporting or removing items.
collectionRemove one paper from a named in-memory reading list by DOI or exact title.
doititlecollectionClear all papers from a named in-memory reading list. Useful when restarting a research branch.
collectionExport the current reading list as citations, abstracts, BibTeX, or an aggregated full-text corpus. Valid `format` values: citations, abstracts, bib, fulltext. Valid `style` values when `format=citations`: harvard, apa, ieee. Use `include_references=true` with `format=fulltext` when you want a richer downstream synthesis corpus.
styleformatenginescollectioninclude_references100% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
git clone https://github.com/laibniz/scholarfetch.gitcd scholarfetchpython3 -m venv .venvsource .venv/bin/activatepip install -e .scholarfetchpython3 scholarfetch.pyELSEVIER_API_KEY=...ELSEVIER_INSTTOKEN=...SPRINGER_META_API_KEY=...SPRINGER_OPENACCESS_API_KEY=.../search graph neural networks/author Albert Einstein/papers 1 has:abstract/article 1/refs 1/saved/export fulltext dummy corpus.txt/papers 1 year>=2020 has:abstract/papers 1 has:fulltext/papers andrea de mauro venue:marketingpython3 scholarfetch_mcp.py --self-testpython3 scholarfetch_fastmcp.py --self-test