"An AI-powered golf swing analysis MCP server" matching MCP servers:

Enables asking natural language questions about local videos, using Whisper transcription, ChromaDB vector storage, and Groq for timestamped answers via MCP.

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.

Enables natural language queries against uploaded OpenAPI/Swagger documentation via a hybrid RAG pipeline, providing grounded answers through MCP-compatible clients like VS Code Copilot.

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.

Interact with WebScraping.AI API for web data extraction and scraping

MCP server that parses aviation accident reports (PDF/DOCX/TXT) and injects their content into extraction prompt templates for knowledge graph construction.

AI customer support MCP server with order status lookup and RAG-powered knowledge base search for e-commerce stores.

A Model Context Protocol server that provides document analysis capabilities to LLM applications, including extraction, chunking, summarization, and semantic search for PDF, DOCX, and plaintext documents.

Enables AI assistants to search a curated database of 200+ AI tool cards with pricing, platforms, use cases, and source links, so users can ask which tool to use for a task and get grounded recommendations.

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.

A persistent, project-scoped memory layer for AI agents, supporting hybrid retrieval (vector, keyword, and tag matching) and sharing across different MCP clients like Claude Code, Qoder, or Cursor.

Extends large language model context length up to 60 times using gravitational memory, designed for Claude Code and Anthropic AI models.

An installable MCP server that reviews diffs/PRs against a target repository's own coding conventions, learned via RAG over its code, docs, and lint config, citing specific file:line sources.

Provides persistent session memory for AI assistants, enabling them to store, search, and retrieve conversation summaries across sessions via the Model Context Protocol.

Extends LLM context windows by 15-60× using gravitational memory compression, enabling massive context extension for codebases, research papers, and long documents.

Ingests pdf and epub documents for LLM-retrieval (RAG)

Provides AI-powered search and documentation tools using Google Vertex AI or Gemini API with real-time web search grounding, enabling technical queries, code analysis, documentation retrieval, and architecture recommendations to overcome LLM knowledge gaps.

A Model Context Protocol server that enables AI models to perform real-time internet and knowledge searches through Higress, enhancing model responses with up-to-date information from Google, Bing, Arxiv, and internal knowledge bases.

Implementation of Model Context Protocol (MCP) server that provides tools for accessing Google Cloud's Vertex AI Gemini models, supporting features like web search grounding and direct knowledge answering for coding assistance and general queries.