Simple Remote MCP Server
Click on "Deploy 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., "@Simple Remote MCP Server say hello to Alice"
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.
Simple MCP Server (FastMCP)
This repository demonstrates how to create, test, and deploy a simple remote MCP server using FastMCP, uv, and GitHub.
Prerequisites
Make sure you have the following installed:
Python 3.10+
uv(Python package manager)Git
VS Code (recommended)
Node.js (for MCP Inspector)
Related MCP server: MCP Template
Step-by-Step Guide
1. Install uv
uv is a fast Python package manager and runtime.
pip install uv2. Create a new project folder
mkdir simple-mcp-server
cd simple-mcp-server3. Open the folder in VS Code
code .4. Initialize the project
uv initThis creates:
pyproject.tomlVirtual environment configuration
5. Install FastMCP
uv add fastmcpFastMCP allows you to build MCP-compatible servers easily.
6. Create a simple server
Create a file called main.py:
from fastmcp import FastMCP
mcp = FastMCP("Simple MCP Server")
@mcp.tool()
def hello(name: str) -> str:
return f"Hello, {name}! Welcome to MCP."
if __name__ == "__main__":
mcp.run()7. Run the server
uv run main.pyYour MCP server will start locally.
8. Test using MCP Inspector
Use MCP Inspector to:
Connect to the server
Verify tools are listed
Send test requests
This confirms your server is MCP-compliant.
9. Create a GitHub repository
Create a new repo on GitHub named:
simple-mcp-server10. Initialize Git locally
git init
git add .
git commit -m "Initial commit: Simple MCP server"11. Add GitHub remote & push
git remote add origin https://github.com/yourusername/simple-mcp-server.git
git push -u origin main12. Deploy on FastMCP Cloud
Create an account on FastMCP Cloud
Connect your GitHub repository
Deploy the project
After deployment:
Your MCP server gets a public endpoint
It can be used by MCP clients and LLM agents
Project Structure
simple-mcp-server/
│── main.py
│── pyproject.toml
│── README.mdNext Steps
Add more MCP tools
Connect this server to LLM agents
Add authentication & logging
Happy building 🚀
Available Tools
2 toolsaddA
Add two numbers together.
Args: a: First number b: Second number
Returns: The sum of a and b
Inputs: a (required), b (required). Returns: result
random_numberB
Generate a random number within a range.
Args: min_val: Minimum value (default: 1) max_val: Maximum value (default: 100)
Returns: A random integer between min_val and max_val
Inputs: min_val, max_val. Returns: result
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: 'add' performs arithmetic addition on two numbers, while 'random_number' generates a random integer within a specified range. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the task.
The naming is mixed: 'add' is a simple verb, while 'random_number' uses a noun phrase with an underscore. This lacks a consistent pattern like verb_noun, but the names are still readable and descriptive enough to understand their functions.
With only 2 tools, the server feels thin and under-scoped for a 'Simple Remote MCP Server' that might imply broader utility. While the tools are functional, the count is too low to cover a meaningful domain, limiting the server's coherence and usefulness for agents.
Inferred as a basic utility server, the tool set is severely incomplete; it lacks common operations like subtraction, multiplication, or other random generation methods (e.g., floats). This creates significant gaps that could lead to agent failures when trying to perform basic mathematical or random tasks.
Maintenance
Related MCP Connectors
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
Nifty's MCP server — exposes tasks, projects, messages, and files as tools for AI agents.
MCP server for your apps' tools and custom tools, plus hosted AI agents and approval-gated workflows
MCP server for building and testing AI agents with multi-model experimentation and insights.
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