MCP ChatBot

@muralianand12345

Repository

Basic information

About MCP ChatBot

Simple MCP Client-Server example

#chatbot #llm #mcp-server #mcp-client #pydantic-ai #fastmcp

MCP ChatBot config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

Repository

MCP ChatBot tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCP-ChatBot?

MCP-ChatBot is a containerized chatbot application that uses the Modular Capability Protocol (MCP) to enable LLM interactions with external services. It integrates a weather service backend and a Streamlit frontend, allowing users to query weather information through natural language.

How to use MCP-ChatBot?

Clone the repository, copy .env.example to .env, add your WeatherAPI and OpenAI API keys, then run docker-compose up --build to launch. Access the Streamlit UI at http://localhost:8501 and type natural language queries about weather (e.g., "What's the weather in New York?").

Key features of MCP-ChatBot

Containerized architecture with separate server and client containers
Real-time weather data via WeatherAPI integration
Clean Streamlit user interface for chatbot interaction
Extensible design ready for additional MCP servers
Powered by OpenAI’s GPT-4o for natural language understanding

Use cases of MCP-ChatBot

Ask about current weather in any city using natural language
Check temperature or conditions for travel planning
Demonstrate MCP protocol with a working example

Frequently asked questions

What are the runtime requirements?

Docker and Docker Compose are required. You need API keys from WeatherAPI and OpenAI.

How do I add a new MCP server?

Create a new server file in the `servers` directory, add the service to `docker-compose.yml`, and update `client.py` to include the new server in the agent configuration.

What should I do if the connection fails?

Ensure all services are up and running. The client has a retry mechanism; if it still fails, restart the application.

Where does the weather data come from?

The weather data is retrieved from the WeatherAPI service using your own API key.

Does the chatbot support any transport besides HTTP/SSE?

The README only shows HTTP/SSE transport via `MCPServerHTTP(url=...)`. No other transports are mentioned.

Comments

More AI & Agents MCP servers

CrawlForge MCP Server

mysleekdesigns

28-tool MCP server for web scraping, crawling, deep research & autonomous extraction — clean Markdown & structured JSON for Claude, Cursor & any MCP client. 1,000 free one-time credits, local-Ollama LLM support.

q-ring

I4cTime

Quantum-inspired keyring for AI coding agents. Secure secrets with superposition, entanglement, tunneling, and teleportation — CLI + 44 MCP tools.

BlazingCDN MCP - Content Delivery for Video, Software & Static Files

BlazingCDN

Official MCP server for BlazingCDN - AI agents (Claude, Cursor, Windsurf) manage CDN resources, purge cache, query metrics, domains, Anycast CDN, Video CDN and Media CDN

OpenZiti MCP Gateway

openziti

NetFoundry MCP Gateway enables secure, isolated access to Model Context Protocol (MCP) tools across distributed systems without exposing public endpoints. Zero trust gateway for MCP servers. Aggregate, filter, and secure

Hostinger

hostinger

Model Context Protocol (MCP) server for Hostinger API. If you don't want to install or run anything locally, connect directly to Hostinger's hosted MCP server: This opens a browser window to authorize via OAuth. Once app

148