"Information about Recorded Future, a cybersecurity company" matching MCP servers:

Enables AI agents to query and manage company knowledge from a Supabase GraphRAG store, including accounts, meetings, people, org hierarchy, product docs, and call logs.

A local AI assistant that answers questions across a company's internal knowledge using MCP, providing controlled access to internal documents with role-based permissions and citations.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

Provides semantic search and access to Contraption Company blog posts and essays. Uses AI-powered embeddings to find relevant content and supports real-time updates via webhooks.

Enables AI coding assistants to search and cite company Markdown knowledge stored in GitLab repositories using a local, read-only index.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

Enables grounded question-answering over internal documents via a single MCP tool that retrieves relevant passages and generates answers with citations, returning sources and diagnostics.

An MCP server that enables AI assistants to access and search Cherry Studio knowledge bases, supporting operations like listing, searching, and retrieving details.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

A local-first RAG system that indexes personal documents and enables querying them via an MCP tool, keeping data private with local embeddings.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

Enables LLMs to access cited company documents and perform permission-gated operational actions (HR, IT access, vendor extraction, renewal) with explicit scope, evidence checks, and durable human approval gates.

A Model Context Protocol server that enables AI assistants like Claude to read from, append to, and format text in Google Documents programmatically.

Provides LLM-free search tools across Confluence, JIRA, code, and project knowledge for any MCP client.

MCP server for integrating LightRAG with AI tools. Provides a unified interface for interacting with LightRAG API through the MCP protocol.