[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_103":104},"routes/modules.$creator.$moduleId",{"_14":15,"_16":17,"_18":19,"_20":21},"routes/modules.$creator.$moduleId.endpoints",{"_11":12,"_13":-5},"error","Module '@laibniz/scholarfetch' is not configured","tools","module",{"_20":22,"_23":24,"_25":26,"_27":28,"_29":30,"_31":32,"_33":26,"_34":-5,"_35":36,"_37":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":48,"_56":57,"_58":59,"_60":-5,"_61":-5,"_62":-5,"_63":-5,"_64":65,"_66":67,"_68":69,"_70":71,"_72":73,"_74":-5,"_75":-5,"_76":-5,"_77":48,"_78":-5,"_79":57,"_80":81,"_82":83,"_84":85,"_86":87,"_88":89,"_90":91},"apiUrl","https://odel.app/api","creator","laibniz","moduleId","scholarfetch","@laibniz/scholarfetch","displayName","ScholarFetch","tagline","Multi-engine scholarly research server for search, traversal, full text, and reading lists.","iconKey","https://avatars.githubusercontent.com/u/78914897?v=4","category","dev-tools","status","published","metaDescription","metaKeywords","deploymentType","external","workerName","bindingName","mcpConfig","{\"url\":\"https://laibniz-scholarfetch-web.hf.space/mcp/\",\"transport\":\"streamable-http\",\"authType\":\"none\"}","pricingModel","free","creatorUserId","system:external-import","createdAt",1781363459000,"updatedAt",1781499692000,"sourceRegistry","official-mcp-registry","sourceUrl","https://registry.modelcontextprotocol.io/v0/servers?search=io.github.laibniz%2Fscholarfetch","sourceVerified",true,"importedAt","upstreamStars",0,"upstreamUpdatedAt",1774289285000,"detectedTransport","detectedAuthType","probeStatus","lastProbedAt","githubRepoUrl","https://github.com/laibniz/scholarfetch","githubRepoFullName","laibniz/scholarfetch","releaseId","ext-laibniz-scholarfetch","version","0.2.1","descriptionMarkdown","# ScholarFetch\n\n![ScholarFetch Logo](https://raw.githubusercontent.com/laibniz/scholarfetch/main/assets/scholarfetch-logo.svg)\n\nScholarFetch is a multi-engine academic research environment for:\n- terminal-first literature exploration\n- MCP-powered agent workflows\n- building curated reading lists and exportable research corpora\n\nIt combines:\n- a rich interactive CLI for humans\n- a classic MCP server (stdio)\n- a FastMCP server (`stdio`, `sse`, `streamable-http`)\n\nThe 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.\n\n## What ScholarFetch Does\n- Searches across multiple scholarly engines in parallel\n- Resolves ambiguous author identities and expands author paper lists\n- Traverses references as first-class research nodes\n- Retrieves abstracts and machine-readable full text when available\n- Tracks a saved paper set during an interactive research session\n- Exports citations, abstracts, BibTeX, or full-text corpora\n- Exposes the same research workflow to MCP agents\n- Maintains stateful saved-paper collections inside one MCP session\n\n## Engines\n- Elsevier (Scopus / Abstract / Article retrieval)\n- OpenAlex\n- Crossref\n- arXiv\n- Europe PMC\n- Springer Nature (metadata + open access)\n- Semantic Scholar (DOI enrichment path)\n\n## Installation\n```bash\ngit clone https://github.com/laibniz/scholarfetch.git\ncd scholarfetch\npython3 -m venv .venv\nsource .venv/bin/activate\npip install -e .\nscholarfetch\n```\n\nConsole scripts:\n- `scholarfetch`\n- `scholarfetch-mcp`\n- `scholarfetch-fastmcp`\n\nAlternative:\n```bash\npython3 scholarfetch.py\n```\n\n## Credentials\nScholarFetch loads provider credentials server-side / client-side from environment.\n\nDefault env file:\n- `.scholarfetch.env`\n\nTypical variables:\n```bash\nELSEVIER_API_KEY=...\nELSEVIER_INSTTOKEN=...\nSPRINGER_META_API_KEY=...\nSPRINGER_OPENACCESS_API_KEY=...\n```\n\nNotes:\n- `ELSEVIER_INSTTOKEN` is optional\n- provider entitlements and rate limits still apply\n- MCP tools do not accept API keys in tool arguments\n\n## CLI Research Workflow\nScholarFetch CLI is designed for research traversal.\n\nTypical flow:\n1. Start from a topic, DOI, or author.\n2. Inspect papers.\n3. Read abstracts or full text.\n4. Expand references.\n5. Jump to related authors.\n6. Save promising papers.\n7. Export a corpus for downstream work.\n\nExample:\n```text\n/search graph neural networks\n/author Albert Einstein\n/papers 1 has:abstract\n/article 1\n/refs 1\n/saved\n/export fulltext dummy corpus.txt\n```\n\n## CLI Features\n- Interactive picker with tree navigation\n- Breadcrumbs for current research position\n- Action bar for `OPEN`, `ABSTRACT`, `TEXT`, `REFS`, and `AUTHOR`\n- `Backspace` to go to parent node\n- `Esc` to return to prompt\n- `S` to save a paper from paper lists or reference lists\n- `X` to remove from the saved list\n- `AUTHOR` action from a paper now lets you select:\n - a single author\n - `ALL AUTHORS`\n- Reference lists behave like paper lists:\n - `open`\n - `abstract`\n - `text`\n - `refs`\n - `author`\n- Automatic paper availability hints:\n - abstract availability\n - full-text availability\n- Progress feedback for expensive transitions\n- Interruptible reference preview building with partial results kept\n\n## Core CLI Commands\n- `/search `\n- `/author `\n- `/papers [filters]`\n- `/doi `\n- `/open `\n- `/abstract `\n- `/article `\n- `/refs `\n- `/ref `\n- `/saved`\n- `/export [format style path ...]`\n- `/import [path]`\n- `/pick [mode]`\n- `/config`\n- `/engines`\n- `/help`\n\n## Paper Filters\nUse with `/papers`:\n- `year>=YYYY`, `year<=YYYY`, `year=YYYY`\n- `has:abstract`, `has:doi`, `has:pdf`, `has:fulltext`\n- `venue:`, `title:`, `doi:`\n\nExamples:\n```text\n/papers 1 year>=2020 has:abstract\n/papers 1 has:fulltext\n/papers andrea de mauro venue:marketing\n```\n\n## Export Modes\nScholarFetch supports four export modes from the saved paper set.\n\n- `bib`\n - BibTeX for citation managers and bibliographic tooling\n- `citations`\n - citation-only export in `harvard`, `apa`, or `ieee`\n- `abstracts`\n - metadata + abstract for each saved paper\n- `fulltext`\n - metadata + abstract + full text when available\n - optional inclusion of references\n\nThis makes ScholarFetch useful as a corpus builder for downstream synthesis agents.\n\n## MCP Server\nScholarFetch exposes the same research model through MCP.\n\nModes:\n- Classic MCP (stdio): `python3 scholarfetch_mcp.py`\n- FastMCP stdio: `python3 scholarfetch_fastmcp.py --transport stdio`\n- FastMCP SSE: `python3 scholarfetch_fastmcp.py --transport sse --host 127.0.0.1 --port 8000`\n- FastMCP streamable HTTP: `python3 scholarfetch_fastmcp.py --transport streamable-http --host 127.0.0.1 --port 8000 --http-path /mcp`\n\nValidation:\n```bash\npython3 scholarfetch_mcp.py --self-test\npython3 scholarfetch_fastmcp.py --self-test\n```\n\nPublic demo endpoints:\n- Web UI: https://huggingface.co/spaces/Laibniz/ScholarFetch_Web\n- Public MCP endpoint: https://laibniz-scholarfetch-web.hf.space/mcp/\n- MCP Registry listing: `io.github.laibniz/scholarfetch`\n\n## MCP Research Model\nThe MCP server is designed for agent workflows, not only one-off calls.\n\nAn agent can:\n1. Search papers\n2. Resolve authors\n3. Expand to author papers\n4. Read abstracts / full text\n5. Expand references\n6. Save promising papers into a named in-memory reading list\n7. Export the reading list as:\n - citations\n - abstracts\n - BibTeX\n - full-text corpus\n\nThis lets an agent build a focused research set inside one MCP session and then hand off an export artifact to another synthesis step.\n\nSee [MCP_SERVER.md](https://github.com/laibniz/scholarfetch/blob/main/MCP_SERVER.md) for the detailed tool model.\n\n## Repository Files\n- `scholarfetch.py`: CLI entrypoint\n- `scholarfetch_cli.py`: core CLI + retrieval logic\n- `scholarfetch_mcp.py`: classic MCP server\n- `scholarfetch_fastmcp.py`: FastMCP server\n- `MCP_SERVER.md`: MCP usage guide\n- `AGENTS.md`: agent-facing workflow guide\n- `SKILL.md`: structured research skill guide\n- `SKILLS.md`: index for agent-facing skill docs\n- `CONTRIBUTING.md`: contributor notes\n\n## For Agents\nIf you are running ScholarFetch from an MCP-compatible system, read:\n- [AGENTS.md](https://github.com/laibniz/scholarfetch/blob/main/AGENTS.md)\n- [SKILL.md](https://github.com/laibniz/scholarfetch/blob/main/SKILL.md)\n- [SKILLS.md](https://github.com/laibniz/scholarfetch/blob/main/SKILLS.md)\n\nThese documents explain how to use ScholarFetch as a literature-research environment rather than as a flat search API.\n\n## Contributing\nSee [CONTRIBUTING.md](https://github.com/laibniz/scholarfetch/blob/main/CONTRIBUTING.md).\n\n## Security\nSee [SECURITY.md](https://github.com/laibniz/scholarfetch/blob/main/SECURITY.md).\n\n## License\nMIT License. See [LICENSE](https://github.com/laibniz/scholarfetch/blob/main/LICENSE).\n","changelogMarkdown","videoKey","requiredSecretsJson","publishedAt","priceCents","trialDays","categoryDisplayName","Developer Tools","upstream",{"_94":95,"_96":97,"_98":99,"_100":101,"_102":-5},"requiredSecrets",[],"tags",[],"assets",[],"stats",{"_92":57,"_93":57},"ratingAvg","reviewCount","topics",[],"license","Apache-2.0","language","Python","registryName","io.github.laibniz/scholarfetch","homepage","ENV",{"_105":106,"_107":108,"_109":110},"ENVIRONMENT","production","TURNSTILE_SITE_KEY","0x4AAAAAAB8-rEI2gNDf_QhU","SENTRY_DSN","https://45fd1229f78089d5c3ee78be128030e1@o4510583261757440.ingest.de.sentry.io/4510584253448272"]