Getting Started with Fluent MCP

This guide will help you get started with Fluent MCP, a framework for building Model Context Protocol (MCP) servers with intelligent reasoning capabilities.

What is MCP?

The Model Context Protocol (MCP) is a standard for communication between language models and the tools they can use. It allows models to choose from available tools and execute them programmatically, extending their capabilities beyond just text generation.

Fluent MCP provides a structured way to build servers that implement MCP, making it easier to create AI-powered applications with tool-using capabilities.

Prerequisites

Python 3.10 or newer
Basic understanding of Python async programming
An API key for your preferred LLM provider (Ollama, Groq, etc.)

Installation

Install the package from PyPI:

pip install fluent_mcp

Or install directly from the GitHub repository:

pip install git+https://github.com/yourusername/fluent_mcp.git

Creating Your First MCP Server

Using the CLI

The quickest way to get started is using the CLI to scaffold a new server:

fluent-mcp new my_first_server

This will create a new directory called my_first_server with the basic structure of an MCP server.

Manual Setup

Alternatively, you can set up a server programmatically:

from fluent_mcp import scaffold_server

scaffold_server(
    output_dir=".",
    server_name="my_first_server",
    description="My first MCP server"
)

Project Structure

After scaffolding, your project will have the following structure:

my_first_server/
├── my_first_server/
│   ├── __init__.py
│   ├── tools/
│   │   └── __init__.py
│   ├── prompts/
│   ├── llm/
│   │   └── __init__.py
│   └── tests/
│       └── __init__.py
├── main.py
└── .env.example

Key components:

main.py: Entry point for your server
my_first_server/tools/: Directory for your tool definitions
my_first_server/prompts/: Directory for your prompt templates
.env.example: Example environment configuration

Configuration

Copy .env.example to .env:

cp .env.example .env

Edit .env with your LLM provider details:

# Server configuration
FLUENT_LOG_LEVEL=INFO

# LLM configuration
FLUENT_LLM_PROVIDER=ollama
FLUENT_LLM_MODEL=llama2
FLUENT_LLM_BASE_URL=http://localhost:11434
FLUENT_LLM_API_KEY=your_api_key_here

Creating Your First Tool

Create a file in the tools directory:

# my_first_server/tools/math_tools.py
from fluent_mcp.core.tool_registry import register_embedded_tool
from typing import List

@register_embedded_tool()
def calculate_sum(numbers: List[float]) -> float:
    """Calculate the sum of a list of numbers."""
    return sum(numbers)

Running Your Server

Make sure to import and register your tools in main.py:

from my_first_server.tools.math_tools import calculate_sum

# Register tools in the server creation
embedded_tools = [calculate_sum]

Run your server:

python main.py

Next Steps

Troubleshooting

Common Issues

LLM client not configured: Make sure your .env file contains the right API key and provider details.
Tool not registered: Ensure your tools are properly imported and registered with the server.
Connection errors: Check that your LLM provider (e.g., Ollama) is running and accessible.

For more detailed help, see the troubleshooting guide.

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