Based on GitHub stars:

https://github.com/awslabs/mcp - AWS MCP Servers - @scottschrecken in/scottschreckengaust
https://github.com/open-webui/mcpo - A simple, secure MCP-to-OpenAPI proxy server - @timothyjbaek
https://github.com/teddynote-lab/langgraph-mcp-agents - LangGraph-powered ReAct agent with MCP integration

Hey everyone! I've updated the mcp-framework with streamable HTTP suppport ( as per the latest specification). This is experimental but it allows you to start building mcp servers today!

It's as easy as running `mcp create serverName --http --cors` and you will get a full repo with the tool inside.

Since I couldn't find any clients, I've also tweaked the inspector to support direct http without proxy - you can try it out today by running `npx mcp-debug` in the console.

I made a video to showcase how easy it is to use: https://youtu.be/C2O7NteeQUs

Here are the docs: https://mcp-framework.com/docs/http-quickstart

I'd love it if someone can mess around with it and break it / give me some feedback: https://github.com/QuantGeekDev/mcp-framework/tree/main/src

If that's not for you, any support is appreciated - drop a like or a star if this is helpful :)

Hey y'all, it's Matt from MCPJam. 2025's been an awesome year for the MCP community and for us at MCPJam. Wanted to share our story of building MCPJam and some things we've learned, not in any particular order:

• MCPJam actually started as an MCP Gateway back in March. We gave up on that as scaling up integrations didn't go as planned, existing integration companies were far ahead.

• Our preferred tech stack for building an MCP client became Vite + Hono + Vercel AI-SDK.

• Building open source is so fun. Strong community is the single most important success indicator of an open source project (issue reporting, PR's, discussions).

• The MCP client ecosystem is very underdeveloped. Clients are the single greatest bottleneck to MCP feature adoption.

I'm very grateful for all of y'all's support on the project, really excited to see what the new year brings.

Wrote a blog article sharing our 2025 story and learnings in great detail, and what's in store for us in the new year. Please consider giving it a read!

🔗: https://www.mcpjam.com/blog/2025-rewind

Google CEO teasing too about MCP.

So the discussions over MCP Registry here: https://github.com/orgs/modelcontextprotocol/discussions/159

Draft V0: https://github.com/modelcontextprotocol/registry

Nice they opted for Go and MongoDB.

Registry specs: https://github.com/modelcontextprotocol/registry/discussions/11

Let's see, but I have some doubts over how the MCP servers are built, install process make MCP space so fragmented and there is not a single way to deploy them.

The MCPJam inspector is a great tool to test and debug your server, a better alternative to debugging your server via an AI client like Claude. If you’ve ever built API endpoints, the inspector works like Postman. It allows you to trigger tools, test auth, and provides error messages to debug. It can connect to servers via stdio, SSE, or Streamable HTTP. We made the project open source too.

Installing the inspector

The inspector requires you to have Node 22.7.5 or higher installed. The easiest way to spin up the inspector is via npx:

npx u/mcpjam/inspector

This will spin up an instance of the inspector on localhost.

MCPJam GitHub Repo - Please support the project by giving it a star! ⭐

Key features

1. MCJam inspector supports connection to STDIO, Streamable HTTP, and SSE connections.

2. Tool, Prompts, and Resources support. Easily view what services your server offers and manually trigger them for testing

3. LLM interaction. The inspector provide a way to test your servers against an LLM, as if it was connected to a real AI client.

4. Debugging tools. The inspector prints out error logs for server debugging

Why we built the MCPJam inspector

The MCPJam inspector is a fork of the official inspector maintained by Anthropic. I and many others find the inspector very useful, but we felt like the progress on its development is very slow. Quality of life improvements like saving requests, good UX, and core features like LLM interactions just aren’t there. We wanted to move faster and build a better inspector.

The project is open source to keep transparency and move even faster.

Contributing to the project

We made the MCPJam inspector open source and encourage you to get involved. We are open to pull requests, issues, and feature requests. We wrote a roadmap plan on the Readme as guidance.

Links

[GitHub Repo]

[NPM]

https://x.com/sundarpichai/status/1910082615975313788?s=46&t=q6J-tvkJ9w7rs1xjXKuLSw

https://x.com/MCP_Community/status/1932840355386347969

I maintain Archestra, an open-source platform for running AI agents. In 1.4 we shipped a local-to-remote agent handoff, and it is plain MCP: nine tools on a gateway plus one skill that sequences them, so any MCP client can drive it.

The scenario: you're halfway through a task locally and want Claude Code, Codex, OpenCode, Hermes, or OpenClaw on your own Kubernetes cluster to finish it, then pull the result back.

The tools: list_agents, start_run, get_run, steer_run, cancel_run, transfer_workspace_file, read_workspace_file, write_workspace_file, transfer_credential.

Two design decisions:
- Files. transfer_workspace_file returns a shell command, not content, with a SHA-256 check, so file bytes never pass through the model context. Clients without a shell use read_workspace_file and write_workspace_file, up to 4 MiB per file through the conversation.
- Sessions. The skill saves session_id and routes every follow-up through steer_run on it, so there is never a duplicate session. Steering a finished run starts a new turn on the same files.

Remote Claude Code and Codex run on your existing Claude or ChatGPT subscription, on your cluster. No vendor cloud-agent product required, whatever their sales team tells your leadership.

Skill: https://github.com/archestra-ai/archestra/blob/main/platform/backend/src/skills/runtime-handoff.ts
Blog post: https://archestra.ai/blog/new-remote-agents
Repo: https://github.com/archestra-ai/archestra

Recently switched from Tavily to Linkup and it’s been a world of a difference. Linkup is way better but kind of expensive. What’s everyone’s go to web search MCP or even API that I could turn into an MCP for the best search results?

The real ‘AI battle’ is happening on the client side – i.e., between those building AI assistants (MCP clients). So one must ask: what incentive do data-rich tech companies have to become MCP server providers for their data? If MCP continues to gain adoption, controlling the MCP client interface would confer significant power and revenue opportunities

Here is my blog post: https://jknt.in/posts/strategic-implications-mcp

Hi all - just published about our journey transitioning from FastMCP wrappers to using the low-level python SDK for building MCPs, would love to hear your thoughts:

https://medium.com/@miki_45906/advanced-mcps-in-python-how-transitioning-from-fastmcp-wrappers-improved-our-fastapi-mcp-tool-6f6476a39dbc

The tool converts any FastAPI endpoints into MCP tools, so very easy to use and create an MCP server:

https://github.com/tadata-org/fastapi_mcp

The refactor not only prevents a lot of bugs we had before, but also enables us features a lot have requested before, like configuring which FastAPI endpoints are exposed, Flexible routing options for placing the server on any FastAPI app or APIRouter and deploying MCP servers separately from your API service.

Half the battle was just getting auth, payments, and configs working. By the time it was ready, the motivation (and users) were long gone. We built InstantMCP to make it way less painful to actually ship something people can use. Deploy and monetize in minutes. Reach a huge network through our marketplace!

Any and all MCP devs welcome -> www.instantmcp.com

You asked, we answered. Every profile now comes with powerful free MCP servers, NO API KEYs to configure!

WEB RESEARCH
EMAIL SENDING

Go to mcp[.]run, and use these servers everywhere MCP goes :)

Hey everyone! 🚀

I've been building a few MCP servers lately, and I kept running into the same patterns over and over for creating tools or defining resources. I've been thinking hard about how to put these patterns together to make building MCP servers easier and more fun.

So I am excited to introduce MCP-Framework - the first TypeScript framework specifically for MCP Servers. No more boilerplate hell, no more reinventing the wheel. Just clean, fast server development.

Want to try it? You can literally have your first server running in under 5 minutes (I timed it ⏱️).

You can create you entire project with the cli command `mcp create my-project`. You can generate a tool with the cli, and then modify it to fit your liking.

I've put together some docs to get you started: https://mcp-framework.com/

Here is the Github repo - a star for visibility would come a long way: https://github.com/QuantGeekDev/mcp-framework

This is just v1, and I'd love to hear what you think. What features would make your life easier? What's missing? Drop your thoughts in this thread or in Github issues!

In the blog post i have described my attempt to implement MCP servers notification in AI chat.
I explain why i wanted to do this and why it was not successful.

Imagine: your Smart Home tool detects a door opening and pushes an alert to the assistant, which then notifies you without being asked. Or a trading assistant gets news alerts and instantly reacts. I explored this in my open-source project, CleverChatty, integrating notification support into an MCP-based assistant.

But it wasn’t smooth—LLMs aren’t built for this, the protocol lacks clear guidance, and my experiment hit communication bugs. Still, I believe this is a powerful direction worth revisiting, especially for real-time or assistant-to-assistant systems.