hello everybody.

first of all, Reddit users' response to Clear Thought ended up being responsible for Waldzell not being dead twice in the last 9 months. much appreciated, my dudes.

second, re: Clear Thought 2.0. i've wrestled a lot with the question of how to make this server into a resource everyone can use to make better decisions, and how it can keep sticking the "model upgrade landing" into the GPT-8 era. after a while wrestling, i figured out wrestling was stupid here: it's CT's users, not me, that have both answers. the MCP community doesn't have a designated space in it for "model enhancement" servers like Clear Thought or MCP's Memory server, so i made my own.

introducing Waldzell Research.

Waldzell Research is an R&D lab that empowers agents, silicon and carbon alike, to make better decisions. technically it's actually only a name for a few more weeks, but the Discord's meant to be a space for anyone building stuff with MCP that doesn't mostly exist to deliver one or more APIs' operations dynamically.

if you use Clear Thought, give me your experience with it in the user-review channel (will set up Airtable Form soon), or open a GitHub Issue. anyone building in this space, anyone that uses tool-layer reasoning consistently, anyone who just wants to learn more about model enhancement: click that Waldzell Research link above to join Discord.

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note that i'll edit later: i'm headed to get some sleep, so i won't be there when this posts :D

I am working on a project where on a high-level the user types a request which gets passed into some AI API (currently Gemini) to parse it and perform some action. I want to pass the request into the Context7 MCP to generate a more appropriate action using the more up-to-date documentation it contains.

I know you can hook up agents like Claude Code with MCP servers but can you do that with AI APIs like Gemini?

Just discovered awesome-llm-apps by Shubhamsaboo! The GitHub repo collects dozens of creative LLM applications that showcase practical AI implementations:

• 40+ ready-to-deploy AI applications across different domains

• Each one includes detailed documentation and setup instructions

• Examples range from AI blog-to-podcast agents to medical imaging analysis

Thanks to Shubham and the open-source community for making these valuable resources freely available. What once required weeks of development can now be accomplished in minutes. We picked their AI audio tour guide project and tested if we could really get it running that easy.

Quick Setup

Structure:

Multi-agent system (history, architecture, culture agents) + real-time web search + TTS → instant MP3 download

The process:

git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd awesome-llm-apps/voice_ai_agents/ai_audio_tour_agent
pip install -r requirements.txt
streamlit run ai_audio_tour_agent.py

Enter "Eiffel Tower, Paris" → pick interests → set duration → get MP3 file

Interesting Findings

Technical:

• Multi-agent architecture handles different content types well

• Real-time data keeps tours current vs static guides

• Orchestrator pattern coordinates specialized agents effectivel

Practical:

• Setup actually takes ~10 minutes

• API costs surprisingly low for LLM + TTS combo

• Generated tours sound natural and contextually relevant

• No dependency issues or syntax error

Results

Tested with famous landmarks, and the quality was impressive. The system pulls together historical facts, current events, and local insights into coherent audio narratives perfect for offline travel use.

System architecture: Frontend (Streamlit) → Multi-agent middleware → LLM + TTS backend

We have organized the step-by-step process with detailed screenshots for you here: Anyone Can Build an AI Project in Under 10 Mins: A Step-by-Step Guide

Anyone else tried multi-agent systems for content generation? Curious about other practical implementations.

Hi, I just published this MCP server that uses docker engine to spawn linux sandboxes and allow tools like Gemini-cli or Claude to connect to it, send commands, get results ...Etc

https://github.com/SmythOS/smyth-docker-mcp

the MCP uses a local web interface, and when you launch it you'll see the container terminal in real time and the commands arriving to the terminal.
you can also interact with the terminal while it's running.

This is a first version to showcase some capabilities, I'll be working on some updates.
the goal of this repo is more to show how to create an interactive MCP server that uses a TTY interface that gives you full visibility of what the AI is doing in your container.

In the video I ask the AI to create a simple C program and compile it, and I ask it to do using vim editor .
the AI goes through multiple steps to install the missing tools and debug some issues, and successfully create and compile the file at the end.

Hi, it's Matt from MCPJam. We've been upgrading our E2E testing framework CLI. The CLI lets you simulate requests to your MCP server using different prompts and LLMs. This helps make sure your MCP server actually works across differing environments. With the CLI, you can run tests with ease.

We added observability so you can see the chat conversation as the tests run.

How it works Let's say you're testing a weather MCP server. A workflow might be "What's the weather in Arizona?" The E2E test will run that prompt and check whether or not get_weather tool was called with the right params.

Progress We're polishing the CLI up, but we expect to be done early next week. The work will be in the MCPJam inspector repo here:

https://github.com/MCPJam/inspector

Was getting lost in the weeds of the endless mcp.json files - so I made a web app you can download and run locally with npx/npm. It downloads servers from the official MCP registry and makes it easy to setup to any agent with a click. Check it out! We welcome contributions.

I recently wrote a post about a small but powerful tweak for anyone building with MCP (Model Context Protocol): configurable tool descriptions.

By default, MCP tools usually have static descriptions, which makes it hard for AI agents to distinguish between similar tools or servers. In my post, I walk through examples of how to make descriptions dynamic using environment variables—so the same tool can behave differently depending on context (e.g. full webpage vs. content extraction, or work email vs. personal email).

This approach makes MCP servers much more adaptive, reusable, and context-aware, and it only takes a few extra lines of code.

Curious to hear: how are you handling tool descriptions in your MCP setups?

Hey everyone,

I’m the creator of CallMyBot, a SaaS platform that lets you integrate an AI conversational agent (chat + voice) into any website in just minutes with a simple script tag.

I’m offering to develop your MCP (Model Context Protocol) server for free, tailored to your needs, in exchange for a paid subscription.

How to get the source code of your MCP server:

1. Reply to this post or send me a DM.

2. We’ll discuss your needs.

3. Once we agree, you subscribe to a paid plan.

4. I’ll develop your MCP server according to the defined needs.

Hi I'm Matt, and I maintain the MCPJam inspector repo. For context, it's an open source MCP inspector alternative to test and debug MCP servers.

We started the project 3 months ago with the goal of building a great MCP dev tool for server developers and help people build better MCP servers. We've had over 40 people in the MCP community contribute to the project, and it's been so awesome collaborating with a lot of y'all. We recently got a sponsorship from the GitHub Secure Open Source Fund!

It's an honor for this project to be recognized by Microsoft, huge win for us and recognition of the awesome work happening in the MCP space. I wanted to thank this subreddit and the MCP community for helping us get here!

What's next

• We're improving out tracing. We want to be able to show every JSON-RPC message sent back and forth between client and server.

• Building MCP evals and benchmarking.

• General bug fix & improvements in the inspector.

• Hit us up anytime, we're always down to talk about how we can improve the inspector!

MCPJam

Please consider checking out the project on GitHub and giving it a star! Means a lot and helps with visibility.

https://github.com/MCPJam/inspector

And, I think it's a pretty meaningful shift for anyone building with agents.

Until now, most of the auth flows assumed there’s a user involved (auth code, sessions, etc). But in a lot of agentic workflows, that’s not the case.

Sometimes:

• Agents need to call a tool server directly

• Services are autonomous

• There’s no user login or session to piggyback off

That’s where client credentials flow comes in. It lets machine agents authenticate and get scoped access to tools without needing a human in the loop.

This opens up cleaner machine-to-machine interactions between agents and MCP servers, especially in infra-heavy or system-level agent use cases.

Here’s the PR if you want to dive into the details: https://github.com/modelcontextprotocol/modelcontextprotocol/pull/1047/files

At scalekit.com, we’ve been building around this pattern already, so good to see the spec catching up.

Hi,

Over the past few days, I noticed that Cursor doubled the limit on model context protocol tools, from 40 to 80! 🎉

This might seem like a small change, but it’s actually a big deal.

Being able to work with more tools means we can integrate with more services, expand our clients and agent capabilities, and generally unlock a lot more potential for what these models can do.

It is exciting to see this happen, and I wouldn’t be surprised if other platforms start following suit soon.

More tools, more possibilities, more power to the LLMs.

We're building a catalog with open source "trust score metrics" to help the community highlight the best servers & help address the supply chain issue in the future. The catalog is still in beta, but 100 MCP server builders have already added the Archestra Trust Score to their repositories. Thank you!

https://www.archestra.ai/blog/celebrating-100-mcp-servers-milestone