MCP (Model Context Protocol) Server

A MCP server that provides a unified interface for interacting with multiple services, currently supporting GitHub, Azure Log Analytics, Google, Azure OpenAI, Azure Vision, PostgreSQL, and more.

🔧 Features

GitHub Integration

- File/Folder Access: Retrieve files or directory listings from GitHub repositories - Workflow Monitoring: Fetch recent workflow runs and their statuses - Code Search: Search for code across repositories - Repository Structure: Get the complete file structure of a repository - Commit History: View commit history with optional file filtering - Commit Diffs: Inspect file-level changes for specific commits

Azure Integration

- Log Analytics Querying: Run Kusto Query Language (KQL) queries against Azure Log Analytics workspaces - Secure Authentication: OAuth2 client credentials flow for secure API access

Utilities

- Timestamp Generation: Get current UTC timestamp with microsecond precision - Website Content: Get the content of a website in Markdown format

Database Integration

- Database Reading: Read a database from a PostgreSQL server

> ⚠️ Use a DB user with SELECT-only privileges

Google Integration

- Google Search: Search Google for a query - YouTube Search: Search YouTube for a query - YouTube Transcript: Get the transcript of a YouTube video

Azure Vision Integration

- Image Analysis: Get the analysis of an image

LLM Integration

- Azure OpenAI: Get a response from Azure OpenAI

📦 Prerequisites

- Python 3.8+
- Docker (optional)
- Tokens & credentials:
- GitHub PAT (repo scope)
- Azure AD App: client ID, secret, tenant ID
- Azure OpenAI Key & Endpoint
- Azure Vision Key & Endpoint
- PostgreSQL credentials

Installation

1. Clone the repository:

   git clone https://github.com/DrFloflo/MCP-github.git

   

2. Install dependencies:

   pip install -r requirements.txt

   

3. Create a .env file in the project root with the following variables:

   # GitHub Configuration

   GITHUB_TOKEN=your_github_token

   

   # Azure Configuration

   AZURE_CLIENT_ID=your_client_id

   AZURE_CLIENT_SECRET=your_client_secret

   AZURE_TENANT_ID=your_tenant_id

# Azure OpenAI Configuration

   AZURE_OPENAI_ENDPOINT=your_azure_openai_endpoint

   AZURE_OPENAI_KEY=your_azure_openai_key

   AZURE_OPENAI_API_VERSION=your_azure_openai_api_version
# Azure Vision Configuration

   VISION_ENDPOINT=your_vision_endpoint

   VISION_KEY=your_vision_key
# Database Configuration

   POSTGRES_HOST=your_postgres_host

   POSTGRES_PORT=your_postgres_port

   POSTGRES_DB=your_postgres_db

   POSTGRES_USER=your_postgres_user

   POSTGRES_PASSWORD=your_postgres_password

   

   

🔐 Token Setup

GitHub

1. Go to GitHub Personal Access Tokens
2. Generate a new token with the repo scope
3. Add this to your .env file:

Azure AD (Log Analytics)

1. Register a new app in Azure Active Directory
2. Copy the following:
- Tenant ID (Directory ID)
- Client ID (Application ID)
- Create and copy a new client secret (under Certificates & secrets)
3. Add to .env:

   AZURE_TENANT_ID=your_tenant_id

   AZURE_CLIENT_ID=your_client_id

   AZURE_CLIENT_SECRET=your_client_secret

   

Log Analytics Workspace

1. Go to your Log Analytics Workspace in Azure
2. Copy your Workspace ID
3. Ensure your Azure AD Application has access:
- Go to your app in Azure AD
- Navigate to API Permissions
- Click Add a permission
- Choose Microsoft Graph > Delegated permissions
- Select User.Read and confirm

Azure OpenAI

1. Go to your Azure OpenAI resource (or Foundry)
2. Copy the endpoint and key
3. Add this to .env:

   AZURE_OPENAI_ENDPOINT=your_azure_openai_endpoint

   AZURE_OPENAI_KEY=your_azure_openai_key

   AZURE_OPENAI_API_VERSION=your_azure_openai_api_version

   

Azure Vision

1. Go to your Azure Vision resource (or Vision)
2. Copy the endpoint and key
3. Add this to .env:

   VISION_ENDPOINT=your_vision_endpoint

   VISION_KEY=your_vision_key

   

PostgreSQL

1. Go to your PostgreSQL server
2. Copy the host, port, database, user, and password
3. Add this to .env:

   POSTGRES_HOST=your_postgres_host

   POSTGRES_PORT=your_postgres_port

   POSTGRES_DB=your_postgres_db

   POSTGRES_USER=your_postgres_user

   POSTGRES_PASSWORD=your_postgres_password

   

Usage

Running the Server

python server.py

The server will start on http://0.0.0.0:6277 by default.

Docker

Build the Docker image:

docker build -t mcp-server .

Run the container:

docker run -p 6277:6277 --env-file .env mcp-server

🧰 Available API Tools

GitHub Tools

- get_github_file_folder: Get file contents or list directories
- get_workflow_runs: Retrieve recent workflow runs
- search_codebase: Search code across repositories
- get_file_structure: Get complete repository structure
- get_commit_history: View commit history
- get_commit_diff: View file-level changes in a commit

Azure Tools

- run_log_analytics_query: Execute KQL queries against Azure Log Analytics

PostgreSQL Tools

- read_db: Query data from PostgreSQL

> ⚠️ Use a database user with read-only (SELECT) access

Google Tools

- search_google: Search Google for any query
- search_youtube: Search YouTube videos
- get_youtube_transcript: Get transcript from a YouTube video

Azure Vision Tools

- get_image_analysis: Analyze image content using Azure Vision

Azure OpenAI Tools

- get_azure_openai_response: Generate response using Azure OpenAI (LLM)

Utility Tools

- get_current_utc_timestamp: Get the current UTC timestamp
- get_website_content: Fetch and convert website HTML to Markdown

Code Tools

- execute_python_code: Execute a Python code snippet securely