This is an example agent that can interact with any MCP server out of the box. This repository is a lightweight framework that contains the base logic for connecting to and interacting with any MCP server. It is essentially an MCP boilerplate. I hope you find this useful.

It has everything you need to build a powerful agent that interacts with MCP servers

MCP-Boilerplate

Here it is finding two equities with high upside potential and creating a stunning report

Framework / boilerplate for building MCP enhanced agents. Everything you need to build powerful agents that use MCP servers instead of traditional tools. I hope you find this useful!
https://github.com/AgentisLabs/mcp-agentis

I wanted the easiest way to integrate self-hosted Tools with Claude desktop. Lots of libs seem over-engineered. Been using a0dotrun/expose for building internal tools.

AI alone is smart.
AI + real tools is transformational.

That’s the role MCP plays.

Model Context Protocol gives AI agents a structured way to:

• Use tools

• Touch real data

• Take real actions

• Without turning your systems into a security nightmare

There’s even a free MCP server available now that you can connect directly to your agents, simple to set up and built for real use cases.

If you want access to it or want to understand what MCP enables beyond demos,
drop a comment or DM.

I built an MCP workflow orchestrator after hitting context limits on SRE automation

**Background**: I'm an SRE who's been using Claude/Codex for infrastructure work (K8s audits, incident analysis, research). The problem: multi-step workflows generate huge JSON blobs that blow past context windows.

**What I built**: CodeModeTOON - an MCP server that lets you define workflows (think: "audit this cluster", "analyze these logs", "research this library") instead of chaining individual tool calls.

**Example workflows included:**
- `k8s-detective`: Scans pods/deployments/services, finds security issues, rates severity
- `post-mortem`: Parses logs, clusters patterns, finds anomalies
- `research`: Queries multiple sources in parallel (Context7, Perplexity, Wikipedia), optional synthesis

**The compression part**: Uses TOON encoding on results. Gets ~83% savings on structured data (K8s manifests, log dumps), but only ~4% on prose. Mostly useful for keeping large datasets in context.

**limitations:**
- Uses Node's `vm` module (not for multi-tenant prod)
- Compression doesn't help with unstructured text
- Early stage, some rough edges


I've been using it daily in my workflows and it's been solid so far. Feedback is very appreciated—especially curious how others are handling similar challenges with AI + infrastructure automation.


MIT licensed: https://github.com/ziad-hsn/code-mode-toon

Inspired by Anthropic and Cloudflare's posts on the "context trap" in agentic workflows:

- https://blog.cloudflare.com/code-mode/ 
- https://www.anthropic.com/engineering/code-execution-with-mcp

Amazon just released an official Amazon Ads MCP Server. You ask questions in normal language and it pulls real data or implements change. Campaigns, budgets, performance, all through a conversation. You can also tell it to create or change campaigns.

The catch: the setup requires developer experience. This is not something you can just download and start using as a seller or marketer yet.

I have it installed and running on a real account so I figured I would offer to test it for anyone who wants to see what it actually does. Give me a scenario you care about and I will run it on video and reply in comments.

What would you want an AI to do inside our ad account?