mcp
by zhkzly
This project provides a simple implementation of the Model Context Protocol (MCP) with Cline as a client and Gemini as a possible LLM. It focuses on demonstrating how to use clients to interact with LLMs and define tools for them.
Last updated: N/A
What is mcp?
This is an implementation of a Model Context Protocol (MCP) server. It allows you to define tools (like Python scripts) that can be called by an LLM through a client like Cline. The server generates these tools and exposes them for use.
How to use mcp?
- Clone the repository. 2. Install the Cline plugin in VSCode. 3. Configure Cline to connect to the MCP server by adding the configuration file. 4. Define tools in the
/src/directory (e.g.,calculate.py,datawale.py). 5. Run the server using a command similar touv --directory /path/to/directory run script.py. 6. Use the Cline client to interact with the LLM and call the defined tools.
Key features of mcp
Tool definition and generation
Integration with Cline client
Support for Gemini LLM
Prompts for defining reusable workflows
Example tools provided
Use cases of mcp
Creating custom tools for LLMs
Standardizing LLM interactions
Sharing common LLM workflows
Building UI for interacting with LLMs
Automating tasks with LLMs
FAQ from mcp
What is MCP?
What is MCP?
Model Context Protocol (MCP) is a protocol for defining and sharing tools and workflows for LLMs.
What is Cline?
What is Cline?
Cline is a client that can be used to interact with MCP servers and LLMs.
How do I define a tool?
How do I define a tool?
Tools are defined as Python scripts in the /src/ directory. You need to define the logic and how it interacts with the LLM.
How do I run the server?
How do I run the server?
Use a command similar to uv --directory /path/to/directory run script.py. Make sure to use absolute paths.
What are prompts?
What are prompts?
Prompts are reusable prompt templates and workflows that clients can easily surface to users and LLMs. They provide a powerful way to standardize and share common LLM interactions.