hootsuite

This MCP server provides a Python-based integration for Hootsuite, exposing a Model Context Protocol (MCP) interface that lets GenAI applications interact with Hootsuite’s API. It includes comprehensive coverage of the Hootsuite API endpoints, multiple authentication methods, and built-in rate limiting with error handling and retry logic to ensure reliable operation in production workloads. The server is designed to run asynchronously for better throughput when handling MCP requests from GenAI applications. To use it, install the Python package, set up the required environment variables for Hootsuite access, and start the MCP server using Python’s module runner. The MCP server exposes endpoints that your GenAI tools can call to manage social posts, streams, scheduling, analytics, and other Hootsuite resources through a consistent MCP interface.

Prerequisites:

• Python 3.8+ installed on your system
• pip (Python package installer) available in your PATH
• Access credentials for Hootsuite (API key, client ID/secret) if required by your setup

Install from PyPI:

pip install hootsuite-mcp-server

Alternatively, install from source:

git clone https://github.com/LokiMCPUniverse/hootsuite-mcp-server.git
cd hootsuite-mcp-server
pip install -e .

Configure environment variables (examples):

export HOOTSUITE_API_KEY=your_api_key
export HOOTSUITE_CLIENT_ID=your_client_id
export HOOTSUITE_CLIENT_SECRET=your_client_secret
export HOOTSUITE_ACCESS_TOKEN=optional_token
export HOOTSUITE_API_BASE_URL=https://api.hootsuite.com

Run the server:

python -m hootsuite_mcp

Tips:

• Ensure Python 3.8+ is used to avoid compatibility issues.
• Store credentials securely; consider a secrets manager or environment file (e.g., .env) loaded at startup.
• If you encounter rate limiting, tune the MCP server's retry settings or implement request backoff strategies compatible with Hootsuite API guidelines.
• The module name (hootsuite_mcp) corresponds to the typical Python package structure; verify the actual entry point if you fork/modify the repository.
• When running in production, consider running behind a process manager (e.g., systemd, PM2) and enable logging for observability.