"Using another LLM for research purposes" matching MCP servers:

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 AI-powered research by breaking a topic into subtopics, gathering information via agents, and compiling a report. Integrates with LangGraph and RAG for orchestration and contextual retrieval.

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 AI coding agents to run Tavily-powered web searches and asynchronous, cited research through MCP, with tools for focused search, task creation, and status retrieval.

Enables an LLM to autonomously maintain a Markdown-based wiki with YAML frontmatter, using hybrid BM25 and local CPU vector search for fully offline retrieval without external APIs or databases.

A deep-research MCP server exposing a single task-augmented tool that runs GPT-Researcher with Tavily search, Anthropic Claude, and OpenAI embeddings, streaming progress via MCP tasks and Upjack entity stream.

Share code context with LLMs via MCP or clipboard

MCP server that answers questions using Reddit discussions as evidence, searching threads, fetching comments, and ranking useful context, with optional cited answer synthesis.

Enables LLM clients to read, search, create, update, and delete notes in a self-hosted shared memory bank over MCP, using hybrid vector and full-text search with optional summarization.

MCP server for grounded, cited AI: answers questions from live web sources, verifies claims, fact-checks documents, searches and reads URLs, summarises, classifies, and extracts fields, with usage tracking and status.

Enables creation of persistent, compounding knowledge bases using Karpathy's LLM Wiki pattern with LLM-maintained markdown wikis. Supports automated ingestion, cross-referencing, synthesis, and linting of sources as an alternative to traditional RAG systems.

Enables AI agents to perform deep web research by searching, fetching, chunking, retrieving, and synthesizing cited answers from exact source passages via a deep_research tool.

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

Give your AI assistant real-time LLM/VLM knowledge. Pricing, benchmarks, and recommendations — updated every hour, not every training cycle.

An open-source deep research MCP server that provides multi-source web search and synthesis with citations, enabling agents to perform citation-backed research using Qwen3-30B-A3B-Thinking and other models.

MCP server that acts as a research intelligence agent, converting natural language queries into SQL or full-text search against Hacker News and arXiv data, and returning answers with supporting evidence.

Enables AI assistants to perform comprehensive research by searching Google, mining Reddit discussions, scraping web content with JS rendering, and synthesizing findings with citations into structured context.

An MCP server that enables saving, retrieving, and managing research content using ChromaDB vector storage and semantic search.

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.