(From someone building with mcp_agent and friends)

Hey Devs 👋

I've been deep in the world of Model Context Protocol (MCP) lately — experimenting, hacking, and exploring what happens when you stop building chatbots... and start building actual agents.

MCP is one of the most exciting things to emerge in AI infra recently — a clean, open protocol for letting language models interact with tools, data, and environments in a standardized way. It gives you a structure to move beyond one-off API calls and finally treat agents like autonomous reasoning systems with access to real-world context.

And here’s the best part: there’s already a growing ecosystem of MCP-based agent apps you can fork, extend, or build on top of today.

The folks at LastMile AI have been doing amazing work open-sourcing examples like mcp_agent_server, mcp_basic_agent, and more. I’ve been experimenting with them myself, and wanted to share 5 apps you can start building on top of right now 👇

1. 🗂️ File + URL Finder Agent

Repo: mcp_basic_agent by LastMile AI

A smart “Finder” agent that decides whether to fetch data from a URL or read from your local filesystem. It uses the MCP fetch and filesystem servers and lets the LLM decide what to use based on the request.

💡 Ideas to build on top:

  • Add PDF, CSV, or Markdown parsing with semantic search
  • Improve retrieval ranking or stream long content
  • Combine local docs with live web data for richer context

Use case: Your own personal research assistant that knows where to look.

2. 🧠 Bedrock-Powered Web Intelligence Agent

Repo: mcp_basic_bedrock_agent

This example swaps in AWS Bedrock as the LLM engine, and uses the same MCP fetch server to retrieve content from URLs. Simple, powerful, and easy to plug into a cloud-native workflow.

💡 Ideas to build on top:

  • Add summarization + classification in a single agent loop
  • Integrate Bedrock toolchains (e.g. Titan, Cohere, etc.)
  • Customize model selection based on task type

Use case: A compliant, scalable research bot for enterprise teams.

3. 💬 Slack Workflow Agent

Repo: mcp_basic_slack_agent

This agent can read and write to Slack and your local filesystem — meaning it can do things like read a file and send it to a Slack channel, or archive Slack messages to disk.

💡 Ideas to build on top:

  • Add Slack thread summarization or channel digests
  • Create alerts triggered by file system events
  • Build a Slack → Notion or Slack → CRM handoff tool

Use case: Build lightweight internal tools without writing frontend code.

4. 🔄 GitHub-to-Slack Agent

Repo: mcp_github_to_slack_agent

A full-on dev tool: this agent monitors GitHub pull requests, analyzes them using an LLM, and posts ranked, readable summaries directly into Slack. It helps teams focus on high-priority changes without drowning in GitHub noise.

💡 Ideas to build on top:

  • Add issue tracking, test coverage, or CI context
  • Flag risky PRs with large diffs or missing reviewers
  • Extend to post weekly digests across multiple repos

Use case: A dev productivity bot that does the reading for you.

5. 🎛️ Model Selector Agent

Repo: mcp_model_selector

This one uses MCP’s ModelPreferences to let an agent dynamically choose which LLM to use — based on priorities like speed, cost, and performance.

💡 Ideas to build on top:

  • Build a smart router that uses different models mid-task
  • Add tracking for token usage or hallucination risk
  • Create a GUI dashboard to compare runs by model

Use case: A foundation for model-routing infrastructure for cost-aware AI apps.

👀 Why This Is Worth Building On

All of these examples were built by the team at LastMile AI to showcase what MCP agents are capable of. They’re open source, composable, and ready to be remixed.

Each app is:

  • Built on top of mcp_agent_server
  • Structured using clean context schemas and tool interfaces
  • Designed to show practical, real-world AI agent workflows

And contributions are welcome💥

🧠 If You’re Curious About Agents...

This is an awesome time to dive in. The protocol is still young, but the ideas are rock solid. Whether you're building a side project or integrating AI into real workflows, MCP gives you a clear foundation for context-aware, tool-using, and outcome-oriented AI.

If you’ve got ideas, questions, or want to collab — feel free to ping me.

And if you build something cool, definitely tag the LastMile team or contribute back.

Let’s go from LLMs to agents — for real.