"A server for academic research" matching MCP servers:

A Python-based agent that integrates research providers (OpenAI, Gemini, DR-Tulu, Open Deep Research) with Claude Code via the Model Context Protocol for automated deep research.

Enables real-time tracking of AI/LLM research progress by searching and aggregating content from arXiv, GitHub, Hugging Face, and Papers with Code. Supports intelligent search, automated daily/weekly research summaries, and covers 15+ AI research areas with smart caching.

Enables comprehensive academic research through the Semantic Scholar API, including paper search, author discovery, citation network analysis, and full-text access from arXiv and Wiley sources.

Enables coding and research agents to run paid, cited deep research on AI infrastructure, MCP, and security topics via remote MCP tools, with query-tied excerpt packs and uncited or failed calls billed at $0.

Enables 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.

Provides a private set of high-level tools for web research, focused URL investigation, read-only GitHub research, and persistent semantic memory via a Streamable HTTP MCP gateway.

Enables AI coding assistants to query private academic paper collections via standard MCP tools, with hybrid retrieval, reranking, and inline citations.

Provides web and local search capabilities via Brave Search combined with advanced academic paper analysis using Google's Gemini model. It allows AI assistants to perform real-time information retrieval and structured research synthesis directly through the Model Context Protocol.

Enables AI-powered academic research workflow from keyword search to hypothesis generation. Integrates multiple AI models to automatically search ArXiv papers, extract key information, and generate innovative research hypotheses for researchers.

Enables AI agents to provide e-commerce customer service with product search, RAG-based FAQ answers, human handoff, and automated security auditing with a review workflow.

A multi-model research agent platform supporting Claude, Gemini, and OpenAI models with web search capabilities, thinking-enabled features, and citation support for advanced research workflows.

Enables LLMs to search academic papers across Semantic Scholar and Crossref, fetch detailed metadata and abstracts by DOI or paper ID, and filter topic searches by publication year. Results are returned as structured responses usable for natural-language research queries.

Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.

Provides AI assistants with real-time web search, reasoning, and research capabilities through Perplexity's Agent API and Search API, enabling tools for search, conversational Q&A, deep research, and advanced reasoning.

Enables grounded, dated research from a curated AI-knowledge repository, offering free BM25 retrieval and a research loop that combines the knowledge base, curated sources, and the open web with tiered citations.

Enables AI agents to run local deep-research workflows via a single MCP stdio server, combining web search, page extraction, query-aware distillation, and caching without cloud quotas. It exposes tools for deep research, search, and single or batch URL reading.

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

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.

Simple, fast, long-term memory for your AI chatbots, agents, and MCP tools. Give your LLM a persistent memory using a local SQLite file. No complex database servers, no heavy cloud setup, and zero C++ compilation errors. Works everywhere.