Fastcat Literature MCP
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- FlicenseCqualityBmaintenanceEnables users to build and query a project-local literature database by ingesting papers from Semantic Scholar, PubMed, arXiv, and OpenAlex, caching open-access PDFs, extracting full text, ranking results, and exposing MCP tools for search, seeds, feedback, and pipeline status over stdio.16-
- AlicenseNot gradedqualityCmaintenanceEnables searching arXiv and Semantic Scholar, fetching and extracting paper text page by page, and retrieving cited excerpts or optionally synthesized answers to questions about papers. It caches results locally and provides PDF page citations for verification.MIT
- AlicenseAqualityBmaintenanceProvides local academic literature search and writing support by querying OpenAlex, arXiv, and Crossref, downloading open-access PDFs, extracting IMRaD sections, and appending BibTeX references.419 PyPI2MIT
- AlicenseAqualityBmaintenanceEnables local literature workflows by searching arXiv, retrieving paper metadata, downloading and parsing PDFs, and retrieving page-cited body evidence for Q&A. It also exposes usage resources and templates for searching, analyzing, and summarizing papers via stdio MCP.4MIT
- AlicenseNot gradedqualityCmaintenanceEnables academic research through one stdio server that searches, reads, and relates papers from a large research index, searches multiple paper providers with PDF retrieval and Markdown conversion, and optionally searches a local Markdown corpus with hybrid retrieval and claim comparison. A unified search tool queries all three sources at once while preserving per-source errors.MIT
- AlicenseNot gradedqualityDmaintenanceEnables searching ArXiv, fetching and indexing papers, and querying a personal paper library using vectorless RAG with BM25 and contextual compression, all without GPU or embedding models.1MIT
TDQS
Scored across 11 tools
Each tool targets a distinct step in the literature workflow: external candidate search, ranking save, publisher-specific download, indexing, semantic retrieval, passage reading, paper listing, legacy extraction, summary reading, and configuration check. Overlaps such as search_papers vs retrieve_evidence are clearly separated by external vs local scope, and extract_paper is explicitly marked legacy and discouraged.
All tool names use snake_case with a clear verb_noun or verb_noun_phrase pattern (index_papers, retrieve_evidence, download_elsevier_paper, check_configuration). The convention is consistent across the entire set with no mixed casing or stylistic breaks.
The 11 tools are well-scoped for a literature RAG server, covering search, download, indexing, retrieval, ranking, and optional legacy summarization without excessive surface area. Each tool has a clear role in the workflow, and the optional legacy tool does not bloat the set.
The core scholarly RAG lifecycle is well covered: external search, ranking, publisher-specific full-text download, indexing, evidence retrieval, passage reading, and optional summarization. Minor gaps exist around generic publisher support, index deletion/cleanup, and metadata export, but agents can work around them for the stated workflow.