ProtoLinkAI 🚀

by StevenROyola

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Simplifying MCP server interactions for seamless AI integration.

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- 🔧 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.

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name ProtoLinkAI 🚀

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

From the repository

pip install ProtoLinkai

---

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

1. Build the Docker image:

   docker build -t ProtoLinkai .

2. Run the container:

   docker run -i --rm ProtoLinkai

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
1. Make the script executable:

bash
chmod +x scripts/run_agent.sh



2. Run the agent:

bash
bash scripts/run_agent.sh



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

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

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.

---

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "protolinkai \ud83d\ude80": {
            "ProtoLink": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "ProtoLinkai",
                    "."
                ]
            }
        }
    }
}

McpServers

{
    "ProtoLink": {
        "command": "docker",
        "args": [
            "build",
            "-t",
            "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.

mcp_architecture
source
---

Installation 📦

Install via PyPI

pip install ProtoLinkai

---

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

---

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:

```dotenv
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

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About

Explore

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

Key Features

Tech Stack 🛠️

🤔 What is MCP?

Installation 📦

Install via PyPI

Usage 💻

Run Locally

Run in Docker

Twitter Integration 🐦

Docker Environment Variables for Twitter Integration

1. Agent Node Client Credentials

2. Tweepy (Twitter API v2) Credentials

Running ProtoLinkAI with Docker

Running ProtoLink with .env + scripts/run_agent.sh

Setting Up Environment Variables

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Running ProtoLink with `.env` + `scripts/run_agent.sh`