Demo MCP server for arXiv-style paper search, using SSE transport and a browser-friendly inspector via a proxy.

Streaming, secure keyword search with line-number results, optimized for large files and memory efficiency.

Context-aware file and directory search with context lines, extension filters, and robust error handling for precise MCP-driven exploration.

Advanced MCP server enabling keyword-based file search with context, line filtering, and flexible matching.

Rust-based Perplexica-powered search with multi-focus modes, chat history, and customizable instructions for contextual, provider-aware queries.

AI-powered Evernote command-line tool enabling natural-language search and retrieval of notes via a Docker-hosted backend.

Arsenal content exposed over the model context protocol with hybrid search, DAG-based composition, and hot-reload indexing for rapid, deterministic data assembly.

Seamlessly access political quotes and transcripts with date filters, speaker analytics, and rich metadata for fast insights.

Unified access to Wikipedia and arXiv with AI-assisted synthesis, caching, and resilient interoperability.

A Perplexity-powered Goose extension enabling sonar-style web search with secure API key handling and minimal, robust setup.

A dedicated MCP server bridging Model Context Protocol with the Qdrant vector store to manage and retrieve model context efficiently.

Provides fast, structured ArXiv paper queries with metadata and abstracts, built with a lightweight SDK for seamless workflow integration.

High-performance DockerHub integration with advanced search, image analysis, tagging, vulnerability checks, and pull-size optimization for AI assistants.

Demonstrates a lightweight, Tavily-backed web search workflow with stdio transport and extensible tooling for rapid model-context experimentation.

Demonstrates a stdio-driven MCP using Tavily web search for rapid Cursor workflows.

Full stack Next.js application, containing all things related to Model Context Protocol.

A multi-agent RAG chatbot that orchestrates document QA via MCP-based messaging, with FAISS-backed retrieval and a Streamlit UI for interactive querying.

Reusable, TypeScript-based template with MongoDB, Typesense, prompts, and tool framework for rapid, production-ready context systems.

YouTube Search App with MCP (Model Context Protocol) integration

Streamlit-based RAG chatbot orchestrating LLM tool calls for document QA with FAISS indexing.

Bridge Elasticsearch to MCP clients via a Python tool, enabling natural-language queries, indexing, and data exploration with easy setup.

Local on-device vector search for PDFs and text with privacy-focused, directory-restricted indexing.

AI-powered search across Komga libraries with OCR and fast indexing for intelligent discovery.

Python tool exposing two Vufind record fetchers: getrecordsuncuyo and get_recordssrnd.