MCP.so
Sign In

Model Context Protocal (MCP) Implementation

@jraa1995

Repository

About Model Context Protocal (MCP) Implementation

This is a simple MCP Server Framework that enables data to be passed through a structured messaging protocol, allowing seamless communication between clients and servers. It supports efficient data exchange, real-time processing, and customizable extensions for various applicatio

#mcp#data-exchange#communication

Basic information

Category

Developer Tools

Runtime

python

Transports

stdio

Publisher

jraa1995

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "Simple-MCP-Build": {
      "command": "python",
      "args": [
        "main.py"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Model Context Protocol (MCP) Implementation?

A framework developed by ClimateGPT Team 1 that implements the Model Context Protocol. It provides core modules for context memory, data loading, query routing, and pipeline execution, designed to manage AI model execution contexts for climate data processing.

How to use Model Context Protocol (MCP) Implementation?

Clone the repository, switch to the ClimateGPT_Team1 branch, set up a Python virtual environment, install dependencies from requirements.txt, then run python main.py. The MCP pipeline is configured via config/config.yaml.

Key features of Model Context Protocol (MCP) Implementation

  • Modular design with context manager, data loader, query manager, and pipeline manager
  • Dynamic query routing and context memory
  • Pipeline execution controlled by config/config.yaml
  • Execution logs stored in logs/mcp_execution.log
  • Includes test EDA and initial climate models

Use cases of Model Context Protocol (MCP) Implementation

  • Running climate scenario projections and temperature trend analysis
  • Managing multi‑step AI model pipelines for climate data
  • Dynamic routing of user queries to appropriate model modules

FAQ from Model Context Protocol (MCP) Implementation

How do I run the MCP framework?

Clone the repo, switch to the ClimateGPT_Team1 branch, create a virtual environment, install dependencies from requirements.txt, then execute python main.py.

How is the pipeline configured?

The pipeline is dynamically controlled by config/config.yaml, which defines the dataset paths and pipeline steps.

Where are execution logs stored?

Logs are written to logs/mcp_execution.log for debugging and tracking execution results.

What are the core modules?

The core MCP components are context_manager.py, data_loader.py, query_manager.py, and pipeline_manager.py.

What models are included?

The repository includes initial models for climate analysis: scenario_projection.py, temperature_trends.py, and Model3.py.

Comments

More Developer Tools MCP servers

MCP-Bridge

SecretiveShell

A middleware to provide an openAI compatible endpoint that can call MCP tools

928

Serena

oraios

A powerful MCP toolkit for coding, providing semantic retrieval and editing capabilities - the IDE for your agent

26.2K

MCP Unity Editor (Game Engine)

CoderGamester

Model Context Protocol (MCP) plugin to connect with Unity Editor — designed for Cursor, Claude Code, Codex, Windsurf and other IDEs

1.8K

mcp-excalidraw

yctimlin

MCP server and Claude Code skill for Excalidraw — programmatic canvas toolkit to create, edit, and export diagrams via AI agents with real-time canvas sync.

2.1K

test

cloudwego

The ultimate LLM/AI application development framework in Go.

12.2K