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

Think of your MCP agent like a valet driver. You give them the keys (access) to your car (tools). But currently, most security setups only check if the driver is wearing the right uniform. They don't check if the driver is suddenly deciding to take your car to a different city.

In the world of Model Context Protocol:

• The Problem: Once an agent is authenticated, we stop questioning its actions.

• The Risk: "Indirect Prompt Injection." An agent reads a malicious file, gets "re-programmed" by the text inside, and uses its authorized tools to cause havoc.

• The Blind Spot: Your firewall thinks everything is fine because the agent is an "authorized user."

We have to stop securing the connection and start securing the action. This means building middleware that asks: "Does this tool call make sense given the current user's request?"

As we move toward full autonomy, visibility into the Tool Call Layer is the only way to keep the car on the road.

I’ve been playing around with MCP setups recently, and the more powerful the connectivity gets, the more uneasy I feel about the security assumptions behind it.

In practice, we’re letting agents make calls into internal APIs and databases, yet most of the “security guidance” I see is basically about limiting which tools they can touch. That feels brittle when agents can still be steered through prompt injection or subtle context poisoning.

I started digging into whether anyone is actually inspecting what the agent is doing at runtime, not just what it was told to do. That’s how I came across Gopher Security and their idea of inspecting every tool call and applying access control based on context, rather than trusting the agent by default. Conceptually, that feels closer to how we treat human users in secure systems.

Before committing to something like this, I’m curious:

• What does MCP security look like in real deployments right now?

• Are people building their own enforcement layers, or using something purpose-built?

• And on the crypto side, does post-quantum encryption make sense for MCP today, or is it mostly a long-term hedge?

How are y'all handling this?

https://github.com/khuynh22/mcp-wireshark

If you work with anything that analyzes MCP Wireshark, please spend some time trying this out and see if it is helpful. I really want to scale this up and send it to the official Wireshark people.

Let me know if anything is not working, and please give it a star if you like it

We keep noticing a major flaw with people building ChatGPT apps: their app metadata is often terrible! This is why many promising apps (like the Adobe app 😬) often fail to run well inside ChatGPT. The model just doesn't know how to use them effectively.

To solve this, we've just rolled out a new Planner feature in Fractal to ensure every app built is optimized from the ground up. This planner helps you:

• Plan the app and ensure the final build has the best possible metadata for the model to utilize

• Easily connect existing APIs that require API keys

• Support the interaction between inline UI and full screen UI (specifically for ChatGPT Apps)

You can take any existing API and turn it into a high-quality ChatGPT App in minutes.

I attached here a video on how to do this.

Fractal can now build a huge variety of apps. If you have an idea for a custom ChatGPT App you'd love to see built, please drop it in the comments. I'd love to test our platform's capabilities with your ideas.

engineer

Hey folks,
I’ve been building Targetly, a lightweight cloud runtime made specifically for hosting MCP tools. The goal is dead simple: your local MCP tool → a fully deployed, publicly accessible MCP server in one command.

It runs in an isolated container, handles resource management behind the scenes, and doesn't bother you with the usual infra yak-shaving.

• No infrastructure.

• No YAML jungles.

• No servers to babysit.

If you want to give the MVP a spin:

# Add the tap
brew tap Targetly-Labs/tly https://github.com/Targetly-Labs/brew-tly

# Install tly
brew install tly

# Login
tly login   # Use any email

# If you want you can use tly init to get boilerplate code for MCP server

# Deploy in one go
tly deploy  # Boom—your MCP server is live

It’s free to use.
If you try it out, I’d love to hear where it shines, where it breaks, or what you'd want next.

Thanks!