"Assessing security risks of inputting sensitive material into a hosted MCP server" matching MCP servers:

Enables AI assistants to search and retrieve structured video material, including dialogue, jokes, story structure, and video details through natural language queries.

Provides MCP tools to search, retrieve, and answer questions from the public Open Finance Brasil Confluence documentation using BM25-based local indexing.

Enables AI agents to interact with Google NotebookLM and Gemini API for document analysis, deep research, and notebook creation with enterprise-grade security including post-quantum encryption and 14 security layers.

Shrimp Task Manager is a task tool built for AI Agents, emphasizing chain-of-thought, reflection, and style consistency. It converts natural language into structured dev tasks with dependency tracking and iterative refinement, enabling agent-like developer behavior in reasoning AI systems.

An MCP server that uses Groq's API to expose raw chain-of-thought tokens from Qwen's qwq model, enabling LLMs to think step-by-step before responding.

This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out our free CData MCP Server for Reckon Accounts Hosted (beta): https://www.cdata.com/download/download.aspx?sku=KHZK-V&type=beta

A scientific reasoning framework that leverages graph structures and the Model Context Protocol (MCP) to process complex scientific queries through an Advanced Scientific Reasoning Graph-of-Thoughts (ASR-GoT) approach.

Enables semantic search over local knowledge bases like Obsidian notes, SQLite, and plaintext files, exposing results to Claude via MCP server.

Provides search and retrieval over a local database of Pathfinder 2e rules. Enables Claude Code to answer rules questions with cited, accurate answers from fetched entries.

Enables AI agents to search and retrieve historical Spanish Call of Duty gaming community posts, with free search previews and pay-per-page citation-ready access via x402.

Enables AI assistants like Cursor and Claude Desktop to access a local AI Material Cabinet for searching, reading/writing content, creating documents, and retrieving chunks via MCP.

Transforms prompts into Chain of Draft (CoD) or Chain of Thought (CoT) format to enhance LLM reasoning quality while reducing token usage by up to 92.4%, supporting multiple LLM providers including Claude, GPT, Ollama, and local models.

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.

Jinni is a tool to efficiently provide Large Language Models the context of your projects. It gives a consolidated view of relevant project files complete with metadata, overcoming the limitations and inefficiencies of reading files one by one. The philosophy behind this tool is that LLM context wi

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.

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

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

Enables interaction with self-hosted LLM models via Ollama and Supabase database operations. Supports text generation, SQL queries, and data storage/retrieval through natural language commands.

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.