An agentic Retrieval-Augmented Generation (RAG) system that combines a small curated machine learning knowledge base with real-time web search capabilities, powered by the Model Context Protocol (MCP).
Enables AI-powered analysis of healthcare market segments, product comparisons, and sales data insights using natural language processing and retrieval-augmented generation.
A full-stack SMS assistant that integrates MessageMedia SMS API with OpenAI's GPT models, enabling AI-driven SMS conversations and message management through function calling and contextual memory.
An MCP server providing 22 pay-per-call utility tools for AI agents (scrape, validate, embed, store, moderate, notify, convert, prevent loops) without accounts or API keys, using USDC payments via the x402 protocol.
MCP server for integrating manufacturing systems (MES/ERP/quality/maintenance) with LLM agents, enabling event ingestion, incident triage, approval workflows, and RAG-based knowledge retrieval.
MCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.
A powerful AI service platform that provides complete MCP tool calling capabilities and RAG knowledge base functionality, enabling users to connect to multiple MCP servers and perform intelligent document search.
A multi-function Streamable HTTP MCP tool aggregation server that provides web search via Brave, Exa, and SearXNG with multi-key rotation and cross-provider fallback, and supports extensible tool families (URL fetch, code search, RAG) through a pluggable architecture.
Enables AI assistants to directly access quant research knowledge, including factor libraries, strategy backtesting, and research reports, through the MCP protocol.
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.
Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.
Guides problem-solving by breaking down complex problems into steps and recommending appropriate MCP tools for each stage, with confidence scores and rationales for tool suggestions.
Provides specialized search capabilities across e-commerce platforms, scientific publications, code repositories, social media, and general web with advanced filtering, including product comparison, research aggregation, and developer tool discovery.
A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.