"Roam Research" matching MCP servers:

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

Enables AI to save, organize, search, and synthesize research materials using a local vector database with support for both OpenAI and Ollama backends.

Enables semantic search and conversational querying across a personal research library of PDFs, DOCX, and other documents using a vector database. It provides tools for document summarization, finding related papers, and high-accuracy retrieval for AI clients like Claude Desktop.

A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.

Semantic search over Nordic economic data — market announcements, quarterly reports (162 companies), macro data (NO/SE/DK/FI), commodity prices, and press releases. 180,000+ vectors.

Enables MCP clients to ingest invoice PDFs into a hybrid-chunked FAISS index, semantically search them, and extract typed invoice data with deterministic arithmetic validation. It also provides listed-company financials and current web search results so agents can research vendors.

Enables Claude to search a local hybrid retrieval index of research papers and ingest new PDFs, providing research-paper memory queryable directly through natural language.

Enables AI agents to autonomously create and manage topic-specific vector knowledge bases with end-to-end functionality including project creation, content ingestion from URLs, semantic search, and progress tracking. Provides a complete research workflow without exposing low-level APIs.

Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.

Enables AI assistants to save, retrieve, and manage research content using ChromaDB vector storage with semantic search, topic organization, and automatic deduplication powered by OpenAI embeddings.

Enables vector search and document retrieval, document-to-Markdown extraction and chunking, and private deep research through configured pipelines.

Enables vector search and document retrieval, text extraction and chunking of documents into Markdown, and private deep research through configured retrieval pipelines.

Enables read-only collection research through six snapshot-backed tools for capabilities, facets, typed selection, ranking, bounded object metadata hydration, and evidence resolution.

Enables agents to perform vector retrieval, extract and chunk documents into Markdown, and generate private deep research reports through configured pipelines.

Enables vector search and document retrieval from pipelines, text extraction and chunking of files into Markdown, and private deep research generation, all backed by Vectorize.

Enables vector search and document retrieval, text extraction and chunking into Markdown, and private deep research workflows through configured pipelines.

Enables users to perform vector retrieval, extract and chunk documents into Markdown, and generate private deep research reports via configured pipelines.

Enables advanced vector retrieval, document text extraction and chunking into Markdown, and private deep research through a configured pipeline.

Enables vector search and document retrieval, extraction and chunking of files into Markdown, and generation of private deep research reports from configured pipelines.

Enables vector search and document retrieval, extraction and chunking of any file into Markdown, and private deep research generation through Vectorize pipelines. It connects MCP clients such as Claude, Cursor, and VS Code to these Vectorize APIs.