What is Protolink

ProtoLink is a standardized tool wrapping framework designed to simplify interactions with MCP servers for seamless AI integration. It allows developers to manage diverse tools in a unified manner, facilitating quick integration and deployment of tool-based use cases.

Use cases

Use cases for ProtoLink include automating Twitter interactions, retrieving cryptocurrency prices, integrating with ElizaOS for conversational AI, providing weather updates, performing dictionary lookups, calculating mathematical expressions, and accessing real-time stock market data.

How to use

To use ProtoLink, you can install it via PyPI using the command ‘pip install ProtoLinkai’. You can run it locally or within a Docker container. For Docker, build the image with ‘docker build -t ProtoLinkai .’ and run it using ‘docker run -i --rm ProtoLinkai’. Configuration for specific tools like Twitter can be done using environment variables.

Key features

Key features of ProtoLink include standardized wrapping for building tools with the MCP protocol, flexible use cases allowing easy addition or removal of tools, and out-of-the-box tools for common scenarios such as Twitter management, cryptocurrency prices, ElizaOS integration, weather information, dictionary lookups, and stock data.

Where to use

ProtoLink can be used in various fields including social media management, financial services, automation of customer interactions, and any application requiring integration of multiple tools or APIs for enhanced functionality.

ProtoLinkAI 🚀

ProtoLink AI is a standardized tool wrapping framework for implementing and managing diverse tools in a unified way. It is designed to help developers quickly integrate and launch tool-based use cases.

Key Features

• 🔧 Standardized Wrapping: Provides an abstraction layer for building tools using the MCP protocol.
• 🚀 Flexible Use Cases: Easily add or remove tools to fit your specific requirements.
• ✨ Out-of-the-Box Tools: Includes pre-built tools for common scenarios:
  • 🐦 Twitter Management: Automate tweeting, replying, and managing Twitter interactions.
  • 💸 Crypto: Get the latest cryptocurrency prices.
  • 🤖 ElizaOS Integration: Seamlessly connect and interact with ElizaOS for enhanced automation.
  • 🕑 Time utilities
  • ☁️ Weather information (API)
  • 📚 Dictionary lookups
  • 🧮 Calculator for mathematical expressions
  • 💵 Currency exchange (API)
  • 📈 Stocks Data: Access real-time and historical stock market information.
  • [WIP] 📰 News: Retrieve the latest news headlines.

Tech Stack 🛠️

• Python: Core programming language
• MCP Framework: Communication protocol
• Docker: Containerization

🤔 What is MCP?

The Model Context Protocol (MCP) is a cutting-edge standard for context sharing and management across AI models and systems. Think of it as the language AI agents use to interact seamlessly. 🧠✨

Here’s why MCP matters:

• 🧩 Standardization: MCP defines how context can be shared across models, enabling interoperability.
• ⚡ Scalability: It’s built to handle large-scale AI systems with high throughput.
• 🔒 Security: Robust authentication and fine-grained access control.
• 🌐 Flexibility: Works across diverse systems and AI architectures.

source

Installation 📦

Install via PyPI

pip install ProtoLinkai

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Usage 💻

Run Locally

ProtoLinkai --local-timezone "America/New_York"

Run in Docker

1. Build the Docker image:
   docker build -t ProtoLinkai .

2. Run the container:
   docker run -i --rm ProtoLinkai

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Twitter Integration 🐦

MProtoLinkAI offers robust Twitter integration, allowing you to automate tweeting, replying, and managing Twitter interactions. This section provides detailed instructions on configuring and using the Twitter integration, both via Docker and .env + scripts/run_agent.sh.

Docker Environment Variables for Twitter Integration

When running ProtoLinkAI within Docker, it’s essential to configure environment variables for Twitter integration. These variables are divided into two categories:

1. Agent Node Client Credentials

These credentials are used by the Node.js client within the agent for managing Twitter interactions.

ENV TWITTER_USERNAME=
ENV TWITTER_PASSWORD=
ENV TWITTER_EMAIL=

2. Tweepy (Twitter API v2) Credentials

These credentials are utilized by Tweepy for interacting with Twitter’s API v2.

ENV TWITTER_API_KEY=
ENV TWITTER_API_SECRET=
ENV TWITTER_ACCESS_TOKEN=
ENV TWITTER_ACCESS_SECRET=
ENV TWITTER_CLIENT_ID=
ENV TWITTER_CLIENT_SECRET=
ENV TWITTER_BEARER_TOKEN=

Running ProtoLinkAI with Docker

1. Build the Docker image:

   docker build -t ProtoLinkai .
   

2. Run the container:

   docker run -i --rm ProtoLinkai
   

Running ProtoLink with .env + scripts/run_agent.sh

Setting Up Environment Variables

Create a .env file in the root directory of your project and add the following environment variables:

ANTHROPIC_API_KEY=your_anthropic_api_key
ELIZA_PATH=/path/to/eliza
TWITTER_USERNAME=your_twitter_username
TWITTER_EMAIL=your_twitter_email
TWITTER_PASSWORD=your_twitter_password
PERSONALITY_CONFIG=/path/to/personality_config.json
RUN_AGENT=True

# Tweepy (Twitter API v2) Credentials
TWITTER_API_KEY=your_twitter_api_key
TWITTER_API_SECRET=your_twitter_api_secret
TWITTER_ACCESS_TOKEN=your_twitter_access_token
TWITTER_ACCESS_SECRET=your_twitter_access_secret
TWITTER_CLIENT_ID=your_twitter_client_id
TWITTER_CLIENT_SECRET=your_twitter_client_secret
TWITTER_BEARER_TOKEN=your_twitter_bearer_token

Running the Agent

1. Make the script executable:

   chmod +x scripts/run_agent.sh
   

2. Run the agent:

   bash scripts/run_agent.sh
   

Summary

You can configure ProtoLink to run with Twitter integration either using Docker or by setting up environment variables in a .env file and running the scripts/run_agent.sh script.

This flexibility allows you to choose the method that best fits your deployment environment.

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ElizaOS Integration 🤖

1. Directly Use Eliza Agents from ProtoLink

This approach allows you to use Eliza Agents without running the Eliza Framework in the background. It simplifies the setup by embedding Eliza functionality directly within ProtoLink.

Steps:

1. Configure ProtoLink to Use Eliza MCP Agent:
   In your Python code, add Eliza MCP Agent to the MultiToolAgent:

   from ProtoLink.core.multi_tool_agent import MultiToolAgent
   from ProtoLink.tools.eliza_mcp_agent import eliza_mcp_agent
   
   multi_tool_agent = MultiToolAgent([
       # ... other agents
       eliza_mcp_agent
   ])
   

Advantages:

• Simplified Setup: No need to manage separate background processes.
• Easier Monitoring: All functionalities are encapsulated within MCPAgentAI.
• Highlight Feature: Emphasizes the flexibility of MCPAgentAI in integrating various tools seamlessly.

2. Run Eliza Framework from ProtoLinkai

This method involves running the Eliza Framework as a separate background process alongside ProtoLinkAI.

Steps:

1. Start Eliza Framework:
   bash src/ProtoLinkai/tools/eliza/scripts/run.sh

2. Monitor Eliza Processes:
   bash src/ProtoLinkai/tools/eliza/scripts/monitor.sh

3. Configure MCPAgentAI to Use Eliza Agent:
   In your Python code, add Eliza Agent to the MultiToolAgent:

   from ProtoLink.core.multi_tool_agent import MultiToolAgent
   from ProtoLink.tools.eliza_agent import eliza_agent
   
   multi_tool_agent = MultiToolAgent([
      # ... other agents
      eliza_agent
   ])
   

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Tutorial: Selecting Specific Tools

You can configure ProtoLink to run only certain tools by modifying the agent configuration in your server or by updating the server.py file to only load desired agents. For example:

from ProtoLinkai.tools.time_agent import TimeAgent
from ProtoLinkai.tools.weather_agent import WeatherAgent
from ProtoLinkai.core.multi_tool_agent import MultiToolAgent

multi_tool_agent = MultiToolAgent([
    TimeAgent(),
    WeatherAgent()
])
This setup will only enable **Time** and **Weather** tools.

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Integration Example: Claude Desktop Configuration

You can integrate ProtoLinkAI with Claude Desktop using the following configuration (claude_desktop_config.json), note that local ElizaOS repo is optional arg:

{
  "mcpServers": {
    "mcpagentai": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "-v",
        "/path/to/local/eliza:/app/eliza",
        "--rm",
        "mcpagentai"
      ]
    }
  }
}

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Development 🛠️

1. Clone this repository:

   git clone https://github.com/StevenROyola/ProtoLink.git
   cd mcpagentai
   

2. (Optional) Create a virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate
   

3. Install dependencies:

   pip install -e .
   

4. Build the package:

   python -m build
   

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License: MIT
Enjoy! 🎉