"Adding a remote MCP server to Claude Desktop using HTTP+SSE" matching MCP servers:

A server for Model Context Protocol (MCP) that uses Server-Sent Events (SSE) for streaming communication, enabling tools like the HackerNews API to be accessed through a secure HTTP+SSE transport.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

reference MCP server over SSE transport — calculator domain, teaching patterns

Provides fast static embedding, similarity, and search capabilities via MCP tools and an OpenAI-compatible HTTP API using a tiny 16M-parameter English embedding model.

Integrates RAGFlow's knowledge base API with Claude Desktop for document retrieval, semantic search across multiple datasets, and intelligent query refinement using DSPy.

A reference implementation for creating an MCP server supporting Streamable HTTP & SSE Transports with OAuth authorization, allowing developers to build OAuth-authorized MCP servers with minimal configuration.

A type-safe solution that enables remote access to Model Context Protocol (MCP) services, allowing clients to connect to centralized MCP implementations without waiting for official remote support.

An HTTP-based Model Context Protocol server for RiotPlan that exposes comprehensive planning tools, resources, and prompts for AI agents. It enables full plan lifecycle management, including ideation, shaping, execution tracking, and retrospective generation.

A plugin that allows Dify to connect to multiple MCP (Model Control Protocol) servers using HTTP with Server-Sent Events transport, supporting custom configurations for URLs, headers, and timeouts.

Bridges STDIO-based MCP clients with SSE-based MCP servers, allowing applications like Claude Desktop to connect to remote MCP servers that use SSE transport.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

A persistent semantic memory system for Claude Code that provides a structured, versioned document store with semantic search and graph visualization. It acts as a memoization layer to store and retrieve research, design decisions, and codebase insights across different work sessions.

A lightweight MCP server that provides Claude Desktop with persistent memory across conversations by storing, summarizing, and retrieving conversation history.

Multi-agent AI consultation framework for Claude Code via MCP — get second opinions from OpenAI, Gemini, Qwen, DeepSeek

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

A custom MCP tool that integrates Perplexity AI's API with Claude Desktop, allowing Claude to perform web-based research and provide answers with citations.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Enables Claude Code to retain persistent cross-session memory with semantic search, automatically tracking user corrections as behavioral patterns that move through lifecycle stages. It also extracts signals from every session to evolve and promote behavioral frameworks, giving the assistant developing judgment over time.

Enables Claude Code sessions to persist identity and cross-session memory through a local SQLite store with semantic search, so past facts, decisions, and lessons can be recalled by meaning. It also detects and classifies user corrections into behavioral patterns that progress from new to internalized, letting reflection cycles evolve skills that work over time.

Enables Claude Code to retain cross-session memory of facts, decisions, and lessons with semantic search, while tracking user corrections as behavioral patterns and evolving self-improving frameworks over time. All processing runs locally through an MCP server and hooks, requiring no cloud service or API key.