"Simple MCP example using HTTP transport" matching MCP servers:

Provides AI coding agents with persistent memory by recording sessions and normalizing them into a searchable knowledge graph, then delivering relevant context at the start of the next session.

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.

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.

Demonstrates an MCP server with tools, resources, and prompts for a knowledgebase chatbot that queries a ChromaDB vector database for RAG responses.

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.

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

Self-hosted knowledge manager and MCP server that lets you organize snippets, questions, and documents via a web UI and exposes them to AI assistants for reading and writing.

A template project for building custom MCP servers that enables direct access to PostgreSQL databases, allowing SQL query execution and schema information retrieval through the Model Context Protocol.

A Model Context Protocol (MCP) server that enables Large Language Models to interact with and query the classic Northwind database through a REST API.

Enables searching and accessing Readwise highlights and documents through HTTP endpoints using the Model Context Protocol. Provides vector and full-text search capabilities with streaming responses for retrieving reading highlights and notes.

HAL (HTTP API Layer) is a Model Context Protocol (MCP) server that provides HTTP API capabilities to Large Language Models.

This MCP server enables any MCP client to perform web searches over HTTP using just an endpoint URL and API key, abstracting away AWS SigV4 signing and Bedrock AgentCore protocol translation entirely server-side.

Enables AI agents and MCP clients to dynamically select few-shot in-context examples using semantic token overlap, cosine similarity, and MMR diversity ranking to optimize prompts deterministically without external dependencies.

An MCP server that extends AI agents' context window by providing tools to store, retrieve, and search memories, allowing agents to maintain history and context across long interactions.

A basic example of a serverless Model Context Protocol (MCP) server implemented using Netlify Functions with Express, enabling AI agents to interact with custom capabilities.

Enables LLMs to store, search, and manage memories with hybrid semantic and keyword search using ChromaDB and Neo4j for persistent memory and knowledge graph capabilities.

Enables hybrid search (BM25 + semantic with RRF) over documentation, with Thai language support. It exposes MCP tools for adding documents and searching via Qdrant and Ollama/OpenAI, using a stateless Streamable HTTP transport.

Enables MCP-compliant clients and autonomous agents to pick optimal few-shot in-context exemplars by combining semantic token overlap, cosine similarity, and Maximal Marginal Relevance diversity ranking for prompt optimization. Runs deterministically on the Python standard library with zero external dependencies, avoiding frontier LLM round-trips for sub-millisecond selection.

Enables creation and management of knowledge graphs with entities, relationships, and observations through HTTP streaming. Supports persistent storage, search functionality, and CRUD operations for building and querying interconnected knowledge bases.

A semantic proxy that reduces AI agent token usage by exposing only three core tools and using local vector embeddings to search for and execute hundreds of underlying MCP tools. It streamlines communication between agents and MCP Routers by identifying relevant tools through natural language queries.