I'm sure you've heard about AI agents at least once in 2025. They're gaining popularity due to their ability to make decisions, automate tasks, and collaborate with humans or other agents—leading to higher productivity.
I built Dissi, an AI agent that can manage your Discord server. It can also perform web searches and post the findings to specific channels or forum posts.
What Are AI Agents?
Credits go to Abhishek Reddy (Medium)
An Ai agent is a software system that autonomously performs tasks on behalf of a user. It uses a Large Language Model (LLM) at its core for understanding, decision-making, and interacting with external environments.
Ai agents are capable of reasoning, acting, and learning in real time.
There are several key components in an Ai agent like:
Tools - functions that an Agent can run like searching the web, running SQL, sending an email or calling APIs.
Storage - help us save Agent sessions and state to a database or file.
Knowledge - domain-specific information that the Agent can search at runtime to make better decisions and provide accurate responses.
What is MCP?
Credits go to Avi Chawla (dailydoseofds)
MCP stands for Model Context Protocol. It is a standardized framework designed to enhance the capabilities of AI agents by enabling them to securely and efficiently access a wide range of external tools such as applications, databases, APIs, and processes.
MCP bridges agents and tools via:
MCP Clients: Interface embedded in AI applications (e.g., IDEs, chatbots) to connect with servers.
MCP Servers: Backend programs that provide tools, data, and context to agents. For example, a Discord MCP server enables server management and sending messages to a discord server.
Tools vs MCP Servers
Developers typically create tools manually to give agents access to external services. This process is tedious and language-dependent.
MCP servers, on the other hand, are easier to build and work with all MCP-compatible agents.
Here’s a quick comparison:
Aspect
Individual Tools
MCP Servers
Scope
Single functionality (e.g., search)
Centralized hub for multiple tools
Integration
Manual coding per tool
Standardized protocol for all tools
Scalability
Complex to add new tools
Add tools via configuration files
Execution Model
Agent directly invokes tools
Server handles tool chaining/autonomy
Currently MCP servers can be built using Python, Typescript, C#, Java and Kotlin.
There are already many MCP servers built by community - List of MCP servers. Maybe check this list before creating your own MCP server!
How to Build Your Own AI Agent?
You might be thinking: "This sounds hard! How do I build my own AI agent as a dev?"
That’s where AI agent frameworks come in.
An AI agent framework is a development toolkit that simplifies the process of building LLM-powered systems.
Meet Agno
Agno is a lightweight library for building Agents with memory, knowledge, tools and reasoning.
Agno provides feature for implementing all the aspects of ai agents. You can add your favorite LLM, storage, knowledge base, tools and even attach MCP servers to ai agents.
It has amazing documentation for building AI agents. Check it out now: Agno Documentation
They also have an amazing community in discord. Make sure to join: Agno Discord
One of my favorite things about Agno is the Agent UI.
It provides a beautiful interface for interacting with your AI agents. You can simply create your AI agent and start using it through Agent UI!
Dissi
Let me introduce Dissi again
Dissi is an AI agent that interacts with your discord servers. It has ability of sending or deleting messages, forum posts, reactions and much more. And it also has access to web search and news tools provided by duckduckgo.
You might be wondering how I got access to so many Discord tools.
Well, here is the secret MCP server I used for Dissi. It provided me a vast array of discord tools.
Of course I used Agent UI to interact with Dissi.
Here’s a simple flowchart to give you a better idea:
Dissi is a high-performance, real-time communication agent powered by Groq and built with Agno, designed to interact with Discord servers using natural language. Equipped with Discord tools and DuckDuckGo search, it allows you to control server actions like reading/sending messages, managing threads, reactions, and more — all from a simple chat UI.
Demo Video
🚀 Installation & Running
1. Setup Environment Variables
Create a .env file based on .env.example and fill in:
GROQ_API_KEY – Your Groq API key
DISCORD_BOT_TOKEN – Your Discord bot token
2. Clone MCP Discord Server Integration
git clone https://github.com/barryyip0625/mcp-discord.git
cd mcp-discord
# Install dependencies
npm install
# Compile TypeScript
npm run build
3. Prepare Python Environment
uv venv
uv sync
4. Clone & Run the Agent UI
npx create-agent-ui@latest
# Enter 'y' when prompted to create a new project# Follow the CLI to install dependencies
Also you can vote for this project in Quira. Voting link
Note: This link may not work properly. So you can first login at quira.sh then click the voting link!
Building Dissi was a fun way to explore what AI agents can really do. With tools like Groq, Agno, and MCP, creating helpful, real-time assistants is more possible than ever.
There’s still a lot to improve, but this is just the beginning. I hope Dissi shows how AI can make everyday tools like Discord more powerful and fun to use.
Let me know if you have any other ideas or if you have built any agents before.
Thanks for reading, and I look forward to connecting with you again soon!
