#sse

Write super fast and memory efficient MCP servers in Zig.

Career and workforce intelligence built on a deep HR ontology — skill taxonomies, role definitions and responsibilities, compensation and incentive structures, learning and development pathways, sourcing strategies, and role/skill evolution mapping. This structured foundation, combined with a RAG knowledge base curated from 50+ premium sources (HBR, McKinsey, BCG, Gartner, Forrester) and updated 3x daily with live web research, powers 6 guided skills and 42 MCP tools for two audiences: working professionals getting personalized career intelligence (CV optimization, salary benchmarking, career strategy), and HR/TA teams running structured talent evaluation, candidate shortlisting, compensation analysis, and consulting-grade workforce research reports. Example Use Cases (for HR/TA teams): 1. Custom Evaluation Models — Train CareerProof on your organization's existing assessment rubrics, scorecards, and evaluation criteria to build custom eval models that evaluate candidates through your specific lens. Upload your competency frameworks and historical assessments, then run inference on new candidates — scored and ranked exactly how your team would, at scale. 2. Candidate Evaluation & Shortlisting — Set up a hiring context with company profile and job description, upload candidate CVs, then batch-rank them with GEM competency scoring and JD-FIT matching. Apply your custom eval models for organization-specific scoring, or deep-dive any candidate with a 360-degree evaluation including tailored interview questions derived from skill taxonomy analysis. 3. Workforce Research Reports — Generate consulting-grade PDF reports across 16 types (salary benchmarking, skills gap analysis, org design, DEI assessment, succession planning, sourcing strategy, and more). Each report is grounded in real-time market data from premium sources and structured around HR ontology — role definitions, compensation structures, L&D pathways, and skill evolution mapping. 4. Compensation & Incentive Benchmarking — Get market-calibrated salary and total compensation intelligence for any role, location, and industry. Analysis is structured around compensation and incentive frameworks from the HR ontology, enriched with live web research and curated knowledge base data covering base salary, equity, bonuses, and benefits. Example Use Cases (for the working professional or career coach): 1. Career Intelligence Chat (Hyper-Personalized) — Ask career strategy questions and get hyper-personalized responses that fuse your CV context with deep insights from the career and workforce RAG knowledge base. Salary benchmarks calibrated to your function and location, industry disruption analysis mapped to your skill profile, and career pivot recommendations grounded in role evolution data — not surface-level answers, but intelligence drawn from the same sources that inform executive strategy. 2. CV Optimization (Hyper-Personalized) — Upload your CV and receive a hyper-personalized positioning pipeline that combines your actual experience with deep insights from our career and workforce RAG knowledge base. Market analysis calibrated to your industry and seniority, career opportunity identification grounded in role/skill evolution data, and targeted edits with trade-off analysis — not generic advice, but intelligence shaped by 50+ premium research sources and your unique career trajectory.

Official MCP server for TrackingTime, the time tracking and productivity platform. Query time entries by user or across your entire team, list projects, tasks, customers, and workspace members. Supports date filtering and pagination. Hosted via SSE — no install needed.

GitHub MCP Server (SSE)

A stdio gateway for the Enterpret's Wisdom MCP SSE Server

MCP Server for Polkassembly API

Node.js MCP server for UpGuard CyberRisk API integration. Provides comprehensive cybersecurity risk assessment tools with stateless HTTP transport, caching, and resilience patterns.

MCP Client 与 MCP Server基于SSE方式的样例实现（Python版本）

Simple MCP Client for remote MCP Servers 🌐

Easy-to-use MCP server framework specialized for SSE.

A simple implementation of MCP Client/Server architecture with HTTP SSE transport layer, dockerized.

A working example to create a FastAPI server with SSE-based MCP support

A working example to create a starlette server with SSE-based MCP support

Example of using MCP servers, both over sdio + sse. Also using langchain-mcp

demo of MCP SSE server limitations using the bun runtime

This is a basic MCP Server-Client Impl using SSE

The SSE version of the MCP service is modified from the Filesystem MCP server

An example of how to implement an MCP server using Elixir and SSE transport

A simple MCP server exposed via SSE with examples tools, resources and prompts

MCP Server using SSE - can be hsoted remotely (build using fastapi, fastmcp, uv)

Grafana SSE Server provides an easy way to query Grafana dashboards and panels using the MCP — enabling natural language querying, monitoring dashboards, and real-time data extraction. sudo docker run -p 8010:8010 \ -e GRAFANA_API_KEY="YOUR_KEY" \ -e GRAFANA_URL="https://grafana.example.com" \ aimiox/amx-mcp-grafana:latest

This repository contains a python implementation of an MCP Host which is capable of running several MCP Servers with SSE protocol

mcp sse server demo

Exposes a SSE MCP server as a rest endpoints for tools. Only for testing

A model-agnostic Message Control Protocol (MCP) server that enables seamless integration with various Large Language Models (LLMs) like GPT, DeepSeek, Claude, and more.

A weather MCP server Demo

The best way to deploy mcp server. A high-performance WebSocket/SSE transport layer & gateway for Anthropic's MCP (Model Context Protocol) — powered by Nginx, Nchan, and FastAPI.

Model Context Protocol Servers with SSE

A simple MCP server that provides weather info

Bun Server Transport implementation for MCP - MCP SSE

基于nest快速开发sse mcp server

A lightweight Model Context Protocol (MCP) server that enables AI assistants like Claude to retrieve and interpret real-time weather data. What makes it popular:

A lightweight implementation of the Model Context Protocol (MCP) server using Server-Sent Events. Features a Wikipedia article parser that converts content to Markdown format. Demonstrates client-server MCP communication over HTTP.

Simple implementation of a Model Communication Protocol (MCP) server using Python over the network using sse transport

Semantic Kernel with MCP server/client sample using SSE transport protocal

Implement SSE to stream agent responses/updates involving the GitHub MCP server

MCP server for understanding AWS spend

MCP Server com SSE e Autenticação Bearer

An MCP Server for generating images via OpenAI's `gpt-image-1`

An MCP service based on the SSE protocol for retrieving the top N Weibo hot searches, callable via API.

A Model Context Protocol (MCP) server for analyzing GitLab repositories and performing security assessments.