Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Finance MCP Serverget the latest stock price and financial metrics for NVDA"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Building a Finance agent with MCP
See Full Video:
Overview
This project demonstrates the use of a Model Context Protocol (MCP) server for retrieving financial data. The MCP server is integrated with Agno and Smol Agent to showcase its versatility in handling multiple agentic frameworks in standardized way.
MCP Server (Finance):
This server is created using
financialdatasets apifetch financial information of companiesStandardizes interactions with external financial data sources using MCP.
Agentic Framework Integration
Integrated mcp server with Agno and Smol Agent.
MCP creates a universal standard for all agentic workflows.
Features
MCP enables AI applications to access diverse data sources and tools using a consistent protocol, streamlining the development process.
AI applications (clients) communicate with MCP servers that expose specific capabilities, such as data access or function execution
MCP allows AI models to retrieve up-to-date information and perform actions based on real-time data, enhancing their responsiveness and accuracy .
Getting Started
Clone the repository:
Add Groq and Financial Datasets APi to .env:
Install UV package Manager
Create Virtual Environment
Activate virtual Environment:
Install dependencies
Start Agno and Smol Agent integrations:
Initialize MCP Inspector
Run
mcp dev server.pyin Terminal
Add MCP server in IDE
License
This project is licensed under the MIT License - see the LICENSE file for details.