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OWL x WhatsApp MCP Server Integration

@Bipul70701

Repository

Basic information

Category

Communication

Transports

stdio

Author

Bipul70701

About OWL x WhatsApp MCP Server Integration

No overview available yet

#whatsapp#mcp#integration#ai-agents

OWL x WhatsApp MCP Server Integration config

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

{
  "mcpServers": {
    "WhatsApp_MCP_Server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

OWL x WhatsApp MCP Server Integration tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

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

Overview

What is OWL x WhatsApp MCP Server?

OWL x WhatsApp MCP Server integrates the WhatsApp MCP server with the OWL multi‑agent framework, enabling AI agents to access, search, and send WhatsApp messages through a Streamlit user interface. It is built for developers who want to combine WhatsApp data with autonomous agent workflows.

How to use OWL x WhatsApp MCP Server?

Clone the repository, create and activate a Python virtual environment, install dependencies with pip install -r requirements.txt, and rename .env_template to .env with the required API keys. Then set up and run the WhatsApp MCP server separately. Finally, launch the Streamlit app with streamlit run project.py.

Key features of OWL x WhatsApp MCP Server

  • Multi‑agent collaboration using CAMEL‑AI and OWL frameworks
  • Access and search personal WhatsApp messages including media
  • Send messages to individuals or groups directly
  • Real‑time web search for up‑to‑date information
  • Intuitive Streamlit user interface

Use cases of OWL x WhatsApp MCP Server

  • Automate responses to incoming WhatsApp messages using AI agents
  • Search and retrieve personal chat history for analysis
  • Dispatch scheduled or automated messages to contacts or groups
  • Combine web search results with WhatsApp data for enriched responses

Frequently asked questions

1

What dependencies are required?

Python, CAMEL‑AI, OWL, Streamlit, and a separate WhatsApp MCP server (follow setup instructions from the linked repository).

2

How do I configure the environment variables?

Rename `.env_template` to `.env` and fill in the required API keys and configurations.

3

Can I send messages through the app?

Yes, the app includes message dispatch functionality to send messages to individuals or groups.

4

Is web search available?

Yes, the SearchToolkit provides real‑time web search capabilities.

5

What does the Streamlit interface do?

It captures user tasks and displays results in real‑time for seamless interaction with the agents.

Comments

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