F

Fluent MCP

Fluent MCP is a modern framework for building Model Context Protocol (MCP) servers with intelligent inference capabilities. It supports AI integration, tool separation, and offloading of complex inferences, and uses a dual - layer LLM architecture for efficient inference.
2 points
10

What is Fluent MCP?

Fluent MCP is a toolkit for building AI servers, especially suitable for creating services that can interact with language models. It provides a structured approach to manage AI tools, perform complex inference tasks, and support the self - improvement of AI systems.

How to use Fluent MCP?

You can create a new MCP server project through a simple command - line tool, and then add custom tools and features. The server can easily integrate language models from different providers and manage the calls of internal and external tools.

Applicable scenarios

Suitable for scenarios where complex AI inference tasks need to be encapsulated into simple APIs, such as intelligent assistants, data analysis services, automated workflows, etc. It is especially suitable for projects that need to hide complex implementation details and only expose simple interfaces.

Main features

Dual - layer LLM architectureOffload complex inference tasks from external LLMs (such as Claude) to internal embedded LLMs to improve efficiency and reduce costs
Tool separationClearly distinguish between internal tools (only available to embedded LLMs) and external tools (exposed to consumer LLMs)
Server scaffoldingQuickly generate the structure of a new MCP server project to accelerate the development process
Prompt managementLoad and manage prompts from files, and support defining available tools in prompts

Advantages and limitations

Advantages
Improve token usage efficiency and reduce API call costs
Hide complex implementation details and provide simple interfaces
Support the self - improvement of AI systems and automatic tool registration
Clear internal/external tool boundaries to improve security
Limitations
Requires certain Python development knowledge
Deploying embedded LLMs may require additional resources
May add unnecessary complexity for simple tasks

How to use

Installation
Install the Fluent MCP package via pip
Create a new server
Use the command - line tool to create a new MCP server project
Define tools
Create internal and external tools in the project
Run the server
Configure and start the MCP server

Usage examples

Research assistantCreate a tool that can answer research questions, using database search and data analysis internally
Math assistantCreate a math calculation service, restricting the use of specific math tools

Frequently Asked Questions

What is the difference between embedded LLMs and consumer LLMs?
How to control which tools are available for a specific prompt?
Which LLM providers are supported?

Related resources

Official documentation
Getting - started guide and detailed documentation
Sample code
Complete examples for various usage scenarios
MIT License
The open - source license used by the project
Installation
Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.
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