How to create an agent that generates its own MCP during planning?

 

Step 1: Understand the Basics of MCP

 

To create an agent that generates its own MCP during planning, it's crucial to first understand what MCP (Model Context Protocol) is. MCP acts as a contract or blueprint between the developer and the model, defining various components such as what the model knows, goals, active contexts, and any necessary guardrails. It is designed to ensure predictable behavior, allow for seamless context swapping, and standardize workflows in multi-agent systems.

 

Step 2: Set Up Your Development Environment

 

Ensure your development environment is equipped with:

• A Python installation (preferably >= 3.7)
• pip for managing Python packages
• An AI framework or library (like OpenAI's GPT, Claude, or another you prefer)
• A text editor or IDE for coding (like VSCode, PyCharm)

Install necessary libraries:

pip install openai  # if using OpenAI's models
pip install langchain  # if leveraging LangChain frameworks
pip install requests  # for any API calls

 

Step 3: Define MCP Components

 

Identify and define the common components of your MCP:

• System Instructions: Specify what the agent is, its purpose, and any primary directives.
• User Profile: Information about the user interacting with the agent, including preferences, name, and goals.
• Document Context: Incorporate relevant knowledge bases or recently accessed documents.
• Active Tasks/Goals: Clarify current objectives or to-dos for the agent.
• Tool Access: Define which external tools the agent can integrate or call upon.
• Rules/Constraints: Any limitations or restricted behaviors for the agent.

 

Step 4: Code Your Agent to Generate MCP

 

Begin coding to enable your agent to dynamically generate its own MCP during planning. Here’s a Python skeleton to get you started:

class Agent:
    def init(self):
        self.mcp = {
            "system_instructions": "You are a language model assistant.",
            "user_profile": {},
            "document_context": [],
            "active_tasks": [],
            "tool_access": [],
            "rules": []
        }

    def generatemcp(self, userdata, documents, tasks, tools, rules):
        self.mcp["userprofile"] = userdata
        self.mcp["document_context"] = documents
        self.mcp["active_tasks"] = tasks
        self.mcp["tool_access"] = tools
        self.mcp["rules"] = rules
        return self.mcp

Usage
agent = Agent()
user_profile = {"name": "Jane Doe", "preferences": {"language": "English"}}
document_context = ["Project requirements", "Design doc"]
active_tasks = ["Plan project"]
tool_access = ["web", "database"]
rules = ["avoid medical advice"]

mcp = agent.generatemcp(userprofile, documentcontext, activetasks, tool_access, rules)
print(mcp)

 

Step 5: Testing and Iteration

 

• Perform Unit Tests: Test each component of the MCP generation process.
• Integration Tests: Ensure that your agent correctly generates MCP in a wider ecosystem or pipeline.
• Iterate and Refine: Adjust component definitions, rules, and processes based on test results and desired outcomes.

 

Step 6: Deploy the Agent

 

After thorough testing, deploy your agent in the desired environment enabling:

• Autonomous Tasks: The agent should perform tasks independently while adhering to the MCP structure.
• Contextual Adaptability: The agent can effectively swap and integrate different MCP components as required.

You can extend the functionality by integrating more libraries or services based on your needs.