yashgarg4/MCP_Server_for_Confluence

Confluence MCP Server

This project provides a powerful, natural language interface for interacting with Confluence. It uses a CrewAI agent powered by Google's Gemini model to understand user prompts and perform actions like creating, searching, and managing Confluence spaces and pages.

The backend is built with FastAPI, and a user-friendly web interface is provided with Streamlit.

🚀 Features

The agent can understand natural language requests to perform the following Confluence actions:

• Create Space: Create a new Confluence space with a given key and name.
• Create Page: Create a new page within a specified space with a title and body.
• Update Page: Update the title or body of an existing page.
• Search Pages: Find pages based on a query or space key.
• Get Page Details: Retrieve the content and details of a specific Confluence page by its ID.
• Add Comments: Add comments to an existing page.
• Delete Pages: Delete a specific page by its ID.
• Expose Context API: Provides structured JSON data about Confluence spaces and pages through dedicated endpoints, making it easy for other tools and agents to consume Confluence information.

🏛️ Architecture

The application consists of two main components that work together:

1. FastAPI Backend (main.py):

   • Exposes an agent endpoint (/invoke) for natural language processing.
   • Exposes a set of context endpoints (/context/...) for serving raw, structured Confluence data.
   • Receives a natural language prompt.
   • Initializes a CrewAI agent ("Confluence Expert").
   • The agent uses the Gemini LLM to decide which Confluence tool to use based on the prompt.
   • Executes the tool and returns the result.

2. Streamlit Frontend (streamlit_app.py):

   • Provides a simple web UI to enter a prompt.
   • Sends the prompt to the FastAPI backend.
   • Displays the agent's final response.

🔌 API Endpoints

The FastAPI server exposes the following endpoints. You can also explore them interactively via the auto-generated documentation at http://127.0.0.1:8000/docs when the server is running.

Agent Invocation

• POST /invoke
  • Description: The primary endpoint to interact with the CrewAI agent. It accepts a natural language prompt and returns the agent's final response.
  • Request Body: {"prompt": "your request here"}
  • Response Body: {"response": "agent's text response"}

MCP Context API

These endpoints provide raw, structured data directly from Confluence, formatted for easy consumption by other applications or agents.

• GET /context/spaces

  • Description: Retrieves a list of all accessible Confluence spaces.
  • Example: curl http://127.0.0.1:8000/context/spaces

• GET /context/pages/{space_key}

  • Description: Retrieves a list of all pages for a specific Confluence space.
  • Example: curl http://127.0.0.1:8000/context/pages/NB

🛠️ Setup and Installation

Follow these steps to get the project running locally.

1. Prerequisites

• Python 3.9+
• Git

2. Clone the Repository

git clone <your-repository-url>
cd confluence-mcp-server

3. Set Up a Virtual Environment

It's highly recommended to use a virtual environment.

# For Windows
python -m venv venv
.\venv\Scripts\activate

# For macOS/Linux
python3 -m venv venv
source venv/bin/activate

4. Install Dependencies

Install all the required Python packages from requirements.txt.

pip install -r requirements.txt

5. Configure Environment Variables

Create a .env file in the root of the project by copying the example file:

cp .env.example .env

Now, open the .env file and fill in your specific credentials:

# .env

# Confluence Configuration
CONFLUENCE_SERVER=https://your-domain.atlassian.net
CONFLUENCE_USERNAME=your-email@example.com
CONFLUENCE_API_TOKEN=your_confluence_api_token

# Google Gemini API Key
GEMINI_API_KEY=your_gemini_api_key

# Backend URL for Streamlit (optional, defaults to localhost)
FASTAPI_BACKEND_URL=http://127.0.0.1:8000/invoke

• CONFLUENCE_API_TOKEN: You can generate this from your Atlassian account settings.
• GEMINI_API_KEY: You can get this from Google AI Studio.

▶️ Running the Application

You need to run the backend and frontend in two separate terminals.

1. Start the FastAPI Backend

In your first terminal, run:

uvicorn main:app --reload

The server will be available at http://127.0.0.1:8000.

2. Start the Streamlit Frontend

In your second terminal, run:

streamlit run streamlit_app.py

Your browser should automatically open to the Streamlit interface.

💡 How to Use

Once the application is running, navigate to the Streamlit URL and type your request into the text area.

Examples:

• Create a new Confluence space with the key 'TEAM' and name 'Team Wiki'.
• Create a new page in space 'NB' with title 'Meeting Notes' and body 'Today we discussed project timelines.'
• Get the details of Confluence page with ID '12345'.
• Search for pages in the 'NB' space that mention 'project timelines'.
• Add a comment to Confluence page '12345': 'This document is ready for review.'