What is the two‑tier LLM architecture?
Fluent MCP uses an embedded LLM for internal reasoning and multi‑step tasks, and a consuming LLM (like Claude) that interacts with the MCP server externally.
@FluentData
基本情報
python package for creating MCP servers with embedded LLM reasoning
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Fluent MCP is a modern framework for building Model Context Protocol (MCP) servers with intelligent reasoning capabilities. It provides a two-tier LLM architecture with tool separation, enabling embedded reasoning and reasoning offloading for token and cost efficiency.
Install with pip install fluent_mcp. Use the CLI command fluent-mcp new my_server to scaffold a new server, or create one programmatically using scaffold_server(). Define embedded and external tools, configure an LLM provider, then run the server with server.run().
Fluent MCP uses an embedded LLM for internal reasoning and multi‑step tasks, and a consuming LLM (like Claude) that interacts with the MCP server externally.
Embedded tools are only available to the embedded LLM for internal use, while external tools are exposed to consuming LLMs through the MCP protocol.
Fluent MCP requires Python. Install it via pip; development dependencies include testing and linting tools (e.g., pytest, flake8, black).
The README does not specify data persistence; the framework manages tool execution and prompt loading, with data handling defined by individual tools.
The README does not detail transport protocols or authentication methods beyond the MCP protocol integration.

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