Fluent MCP
by FluentData
Fluent MCP is a modern framework for building Model Context Protocol (MCP) servers with intelligent reasoning capabilities. It provides a structured approach to building servers that can perform embedded reasoning with language models, register and execute tools, and manage prompts and configurations.
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What is Fluent MCP?
Fluent MCP is a toolkit for scaffolding and managing MCP servers with a focus on AI integration. It enables the creation of servers that can perform embedded reasoning with language models, register and execute tools, and manage prompts and configurations, supporting the development of self-improving AI systems.
How to use Fluent MCP?
To use Fluent MCP, you can install it via pip (pip install fluent_mcp). You can then scaffold a new server using the CLI or programmatically. Define embedded and external tools, implement the core architecture pattern (two-tier LLM architecture, tool separation, and reasoning offloading), and run the server. The framework also supports prompt management with tool definitions in frontmatter.
Key features of Fluent MCP
Reasoning Offloading
Tool Separation
Server Scaffolding
LLM Integration
Tool Registry
Embedded Reasoning
Prompt Management
Error Handling
Use cases of Fluent MCP
Building AI agents with reasoning capabilities
Creating specialized AI services with internal and external tools
Developing self-improving AI systems
Offloading complex reasoning from consuming LLMs to embedded LLMs
FAQ from Fluent MCP
What is an MCP server?
What is an MCP server?
An MCP (Model Context Protocol) server acts as an intermediary between a consuming LLM (like Claude) and internal tools and reasoning capabilities.
What is embedded reasoning?
What is embedded reasoning?
Embedded reasoning refers to the internal reasoning processes performed by an LLM within the MCP server, leveraging embedded tools and prompts.
What are embedded and external tools?
What are embedded and external tools?
Embedded tools are internal tools available only to the embedded LLM for reasoning. External tools are exposed to consuming LLMs through the MCP protocol.
How does Fluent MCP improve token efficiency?
How does Fluent MCP improve token efficiency?
By offloading complex reasoning to the embedded LLM, consuming LLMs use fewer tokens, reducing costs.
How do I define which tools are available to a prompt?
How do I define which tools are available to a prompt?
You can define the available tools directly in the prompt's frontmatter using the tools key.