"A server for finding academic or research papers" matching MCP servers:

Enables LLMs to search and retrieve academic papers from arXiv by topic, author, category, or ID.

MCP server for scientific grounding: search and discover open-access research papers across arXiv, OpenAlex, Crossref, PubMed, and Semantic Scholar, and retrieve references/citations from paywalled journals via public DOI/abstract metadata.

Monitors PubMed and medRxiv for configurable research topics, summarizes papers using a local LLM, and stores deduplicated results in a local database.

Enables searching academic literature via Semantic Scholar with relevance filters, batch metadata retrieval, and citation-graph recommendations.

Unified academic search MCP server that searches open literature (arXiv, bioRxiv, medRxiv, PMC), CNKI, and Web of Science, with browser-backed authentication, local paper library, and export to multiple formats.

A multi-step academic figure agent harness that enables AI agents to plan, generate, evaluate, and iterate publication-grade figures from sources like PMIDs, preprints, or freeform briefs.

Enables semantic search across scientific papers in your Zotero library with hybrid search, incremental indexing, and cross-encoder reranking.

A unified MCP server for academic paper discovery, citation exploration, and research intelligence workflows over multiple scientific knowledge sources.

A read-only FastMCP server for academic research that lets agents search papers, inspect metadata, follow citation graphs, generate BibTeX, and read full texts of open-access PDFs via semantic scholar, OpenAlex, arXiv, DBLP, and Crossref.

Enables AI models to search and retrieve bibliographic and digitized records from Swiss academic libraries (swisscovery, e-rara, e-periodica, e-manuscripta) via open protocols without requiring API keys.

MCP server enabling AI agents to search and retrieve scientific papers, citations, and author profiles from Crossref, OpenAlex, and Semantic Scholar with no API keys required.

An MCP server that enables AI assistants to call statistician's decision logic for recommending statistical tests, checking assumptions, interpreting results, and planning sample sizes.

Enables users to conduct evidence-driven product research through MCP by importing documents, extracting JavaScript code clues, recording cited facts and inferences, tracking validation, managing research lifecycles, and exporting Markdown or JSON reports. It provides 25 MCP tools for turning source materials into traceable research findings and verification plans.

Exposes fourteen read-only tools over the HEY Research public API, letting assistants look up Robinhood Chain projects, builders, shipped work, relationships and market context, with each answer tagged FACT, DERIVED or UNKNOWN and unmeasured fields omitted rather than zeroed. Runs over stdio or hosted Streamable HTTP, and gives no trade instructions.

Research methods, workflow tools, and independent review for AI agents, packaged as a Skill, plugin, and local MCP server.

Enables MCP clients like Claude to query Chinese A-share market data, including K-line charts with forward/backward adjustment, limit-up and limit-down pools, per-stock fund flows, index quotes, and stock news, with automatic symbol normalization and retry handling for throttled data sources.

Provides A-share (Chinese stock market) quantitative analysis through tools for stock screening, northbound capital flow tracking, dragon-tiger list analysis, margin trading, sector analysis, technical indicators, IPO info, and limit-up/down statistics using akshare data.

Enables agents to run a closed loop of searching, deduplicating, scoring, and storing scholarly records from Web of Science, Wanfang, VIP, and CNKI, then enrich metadata and download full-text PDF/CAJ via the user's institutional sessions. Also exports citations in 13 formats including GB/T 7714, APA, BibTeX, RIS, and CSL-JSON.

Enables students, researchers, and institutions to automate the creation, structuring, iterative versioning, and methodology validation of formal academic thesis and research proposals.

AI-operable research workspace integrating Zotero, Obsidian, and NotebookLM. Search papers (arXiv/Semantic Scholar/PubMed/CrossRef), ingest into Zotero, sync per-paper notes to Obsidian, verify NotebookLM briefs. All three external tools optional.

提供A股实时行情、历史K线、财务报表、技术指标等全方位数据查询的MCP服务器，支持盘中即时更新及批量查询。

Enables federated multi-source research by fanning out queries across web, community, and social sources, deduplicating and interleaving results, and storing a persistent corpus for cross-checking.

Enables semantic search across personal PDF paper collections with page-level citations, allowing users to query their library from any MCP-capable client.

Browser-based research MCP server that drives a real Chromium browser via patched Playwright to access JavaScript-rendered content, dynamic tables, and login-walled dashboards. It provides tools for visiting URLs to extract DOM text and screenshots, and for performing structured data extraction using Anthropic Claude Sonnet.

Enables searching and reading of AI/ML papers from conference proceedings and arxiv, with tools for full-text search, table of contents, and content grep.

An MCP server exposing a registry of paper-backed quantitative trading methods plus a deterministic, no-LLM decision helper for reproducible trading research.

Enables producing verifiable FORRT nanopublication chains with grounded quote verification and compact Science Live constellation projections, supporting reproduction, replication, and new research workflows.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

Provides machine learning researchers with tools for creating publication-quality scientific visualizations, statistical plots, and 2D data representations. It streamlines the research workflow by enabling AI assistants to generate complex figures from CSV, JSON, or direct data inputs.

Recommends study designs for a research question, states the identifying assumptions to verify, and returns runnable R, Python, or Julia code with a formatted citation for every source, drawn from methodological textbooks, an R package, and research articles on causal inference, econometrics, statistics, and machine learning.