init0 (u/init0) - Readit News

init0 commented on MCP Web Host mcphost.link/... · Posted by u/init0

init0 · 14 days ago

Hey folks! I just launched https://mcphost.link/ a web-based MCP host that lets you connect to multiple remote MCP servers and interact with them through a simple chat-style interface.

Key Features

Multi-server support — connect to several MCP servers at once

OAuth 2.0 & Bearer Token auth (with PKCE)

Persistent sessions — servers + credentials saved locally

Full MCP features — tools, resources, prompts

LLM support — bring your own inference backend

The goal is to make exploring and working with the Model Context Protocol much more approachable.

Happy to answer questions, take feedback, or hear feature requests!

init0 commented on SEP-1865 MCP Apps Extension sample implementation github.com/hemanth/mcp-ex... · Posted by u/init0

init0 · 17 days ago

SEP-1865 MCP Apps Extension, even though in draft currently, will change how AI agents deliver interactive experiences.

The idea: MCP tools return HTML/CSS/JS directly. The client renders it in a sandboxed iframe. That's it.

Your AI agent calls a tool, gets back a full interactive UI. Dashboard, form, chart - whatever you need.

How it works: - Tool returns text/html+mcp resource - Client renders in iframe with CSP - UI talks back via JSON-RPC 2.0 postMessage - Fully sandboxed, secure by default

Built a sample implementation with vanilla Web Components. This is where MCP is heading.

init0 commented on Agentu: The sleekest way to build AI agents pypi.org/project/agentu/... · Posted by u/init0

init0 · a month ago

I got tired of complex agent frameworks with their orchestrators and YAML configs, so I built something simpler.

  AgentU uses two operators for workflows: >> chains steps, & runs parallel. That's it.

``` from agentu import Agent, serve import asyncio

  def search(topic: str) -> str:
      return f"Results for {topic}"

  # Agent auto-detects available model, connects to authenticated MCP server
  agent = Agent("researcher").with_tools([search]).with_mcp([
      {"url": "http://localhost:3000", "headers": {"Authorization": "Bearer token123"}}
  ])

  # Memory
  agent.remember("User wants technical depth", importance=0.9)

  # Parallel then sequential: & runs parallel, >> chains
  workflow = (
      agent("AI") & agent("ML") & agent("LLMs")
      >> agent(lambda prev: f"Compare: {prev}")
  )

  # Execute workflow
  result = asyncio.run(workflow.run())

  # REST API with auto-generated Swagger docs
  serve(agent, port=8000)

```

  Features:
  - Auto-detects Ollama models (also works with OpenAI, vLLM, LM Studio)
  - Memory with importance weights, SQLite backend
  - MCP integration with auth support
  - One-line REST API with Swagger docs
  - Python functions are tools, no decorators needed

  Using it for automated code review, parallel data enrichment, research synthesis.

  pip install agentu

  GitHub: https://github.com/hemanth/agentu

  Open to feedback.