MCP Server Client
by muralianand12345
The MCP Server Client project outlines a system architecture involving multiple services and tools, including a frontend, API, and various backend components. It leverages services like OpenAI for chat completion and embedding, and incorporates tools like MCP servers for specific tasks.
Last updated: N/A
What is MCP Server Client?
This project appears to be a multi-component application that utilizes a NestJS API to connect a Streamlit frontend with various services, including chat, agent, and RAG (Retrieval-Augmented Generation) functionalities. It integrates with OpenAI for language model capabilities and uses MCP servers for tool execution.
How to use MCP Server Client?
Based on the provided architecture, users interact with the system through a Streamlit frontend. The frontend sends queries to the NestJS API, which orchestrates the various services to process the request. The API then returns a response to the frontend for display to the user. Specific usage instructions for the MCP servers would depend on their individual functionalities.
Key features of MCP Server Client
Streamlit Frontend
NestJS API
Chat Service
Agent Service
RAG Service
Tool Agent Service
OpenAI Integration
PGVector Storage
MCP Server Integration
Use cases of MCP Server Client
Chatbot applications
Question answering systems
AI-powered tool execution
Data retrieval and generation
Integration with external APIs
FAQ from MCP Server Client
What is the role of the MCP servers?
What is the role of the MCP servers?
The MCP servers appear to be responsible for executing specific tools or tasks, potentially interacting with external resources like AWS S3 and PostgreSQL.
How does the RAG service work?
How does the RAG service work?
The RAG service likely retrieves relevant information from a knowledge base (PGVector) based on the user's query and uses OpenAI to generate a context-aware response.
What is the purpose of the Agent Service?
What is the purpose of the Agent Service?
The Agent Service likely uses OpenAI's chat-completion API to create intelligent agents that can interact with users and perform tasks.
What is LocalStack used for?
What is LocalStack used for?
LocalStack is used as a mock AWS S3 service, likely for storing and retrieving data related to the MCP servers.
How is data stored?
How is data stored?
Data is stored in PGVector for embeddings, PostgreSQL for general data, and potentially in AWS S3 (via LocalStack) for data related to the MCP servers.