Remote MCP Server and Client Showcase
A practical Model Context Protocol workspace with two connected parts:
- A remote-ready FastMCP expense server for structured expense tracking.
- A Streamlit MCP client app with local math tools, web search, Groq/OpenAI LLM support, and Manim animation rendering.
This repo is designed as a compact reference for building MCP tools, exposing them through a server, and consuming them from a chat UI.
What This Shows
| Area | What is included |
|---|---|
| MCP server | FastMCP tools for expense management with SQLite persistence |
| MCP client | Streamlit chat app using langchain-mcp-adapters |
| Tool calling | Math tools, Tavily-backed search, and Manim video rendering |
| LLM providers | Groq by default, OpenAI available through config |
| Local testing | Smoke tests for MCP tools, search backend, and Manim rendering |
Repository Structure
test-remote-mcp-server/
|-- main.py # FastMCP expense server
|-- proxy.py # Proxy entrypoint for remote access
|-- categories.json # Expense category definitions
|-- pyproject.toml # Server dependencies
|-- uv.lock # Server lockfile
|-- mcp-client-app/ # Streamlit MCP client showcase
| |-- client2.py # Main web app
| |-- main.py # Local math MCP server
| |-- test_tools.py # Tool smoke tests
| |-- manim_test_scene.py # Direct Manim render test
| |-- .env.example # Safe environment template
| `-- README.md # Client app guide
`-- README.md
FastMCP Expense Server
The root server exposes expense tracking tools backed by SQLite. It is useful for testing remote MCP tool workflows and structured tool arguments.
Server Setup
git clone https://github.com/deepakbishnoi717/test-remote-mcp-server.git
cd test-remote-mcp-server
uv sync
Run the local server:
uv run python main.py
Run the proxy entrypoint:
uv run python proxy.py
Expense Tool Example
Use natural language from an MCP-compatible client:
Add an expense for 450 INR in Food, subcategory Lunch, with note "team meal".
Streamlit MCP Client App
The client app lives in mcp-client-app/. It demonstrates a chat UI that can call tools from both an MCP server and direct LangChain tools.
Highlights:
- Local MCP math server:
add,subtract,multiply,divide - Tavily-backed web search tool exposed as
brave_search - Manim renderer exposed as
render_manim_code - Groq model support by default
- OpenAI fallback through
.env - Windows-friendly launcher:
run_app.bat
Start the client:
cd mcp-client-app
copy .env.example .env
run_app.bat
Open:
http://localhost:8501
Demo Prompts
Try these in the Streamlit app:
Use the math tool to multiply 12 by 8, then subtract 10.
Search the web for the latest Model Context Protocol updates and summarize them.
Use render_manim_code to create a Manim animation of a blue circle transforming into a green square. Return the rendered video path.
Validation
From the client folder:
.\.venv\Scripts\python.exe -B test_tools.py
Direct Manim render test:
.\.venv\Scripts\python.exe -B -m manim -ql manim_test_scene.py GeneratedScene --media_dir manim_outputs\direct_media
Expected Manim output:
manim_outputs\direct_media\videos\manim_test_scene\480p15\GeneratedScene.mp4
Environment Variables
The client app uses .env.example as a safe template:
| Variable | Purpose |
|---|---|
GROQ_API_KEY | Required for Groq chat models |
TAVILY_API_KEY | Required for web search |
OPENAI_API_KEY | Optional OpenAI provider |
LLM_PROVIDER | groq or openai |
GROQ_MODEL | Default Groq model |
OPENAI_MODEL | Default OpenAI model |
Never commit a real .env file.
Tech Stack
- FastMCP and MCP
- LangChain tool binding
- Streamlit
- Groq and OpenAI chat providers
- Tavily Search API
- Manim Community
- SQLite
- uv
Notes
- The root server and the client app are intentionally separated so each can be studied or deployed independently.
- Generated videos and local virtual environments are ignored by Git.
- The client defaults to Groq to avoid OpenAI quota errors during local testing.