study-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@study-mcpadd 5 and 3"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Model Context Protocol (MCP)
Installing Dependencies
First, install the dependencies:
uv install -r requirements.txtRunning the Server
To run the server, execute the following command:
uv run main.pyAfter running the server, you must to use or MCP Inspector or any other client to interact with the server. The server will be listening on http://localhost:8000/mcp.
To run the MCP Inspector, execute the following command:
npx -y @modelcontextprotocol/inspectorSet the URL to http://localhost:8000/mcp and click "Connect". You should see the server's capabilities and be able to send requests to it.
Concepts
Inside the MCP, we have three main concepts: Resources, Tools, and Prompts.
Resources are used to represent data that can be accessed by the model. They are defined using the
@mcp.resourcedecorator and can be accessed using a URL-like syntax. For example,greetings://{name}is a resource that generates a personalized greeting based on the provided name.Tools are functions that perform specific actions or calculations. They are defined using the
@mcp.tooldecorator and can be called by the model to perform tasks. For example, theaddtool takes two numbers and returns their sum.Prompts are used to generate prompts for the model. They are defined using the
@mcp.promptdecorator and can be used to create dynamic prompts based on input parameters. For example, thecalculate_promptprompt generates a prompt for performing a mathematical operation based on the provided operation and numbers.
Usually after call a prompt, it will redirect to a resource or a tool, but it can also return a string that can be used as a prompt for the model.
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Resources
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