Building Dissi with Agno and MCP

Why AI agents are hot right now ? 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: You can check out the GitHub repository here: kom-senapati / dissi

Apr 19, 2025 - 07:46

Why AI agents are hot right now ?

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:

You can check out the GitHub repository here: