MCP_AGENT_IMPLEMENTATION.md

Simple MCP Agent Implementation Summary

🎯 What You Wanted

A simple LangChain agent where:

1. Each bot has its own MCP server tools
2. The LLM automatically decides which tools to use
3. Tools are executed automatically based on the query

✅ What I Implemented

1. MCPAgentService (/src/botforge/services/mcp_agent_service.py)

# Simple agent that follows your pattern exactly:
llm = OpenAI(temperature=0)
tools = [calculator_tool, mcp_tools...]  # Bot's MCP tools
agent = initialize_agent(tools, llm, AgentType.ZERO_SHOT_REACT_DESCRIPTION)
response = agent.run(input=query)  # LLM decides and executes tools automatically

2. API Endpoint (POST /query-with-agent)

{
  "bot_id": "7e57e5b1-994a-44e4-973e-ed83ffa0ea51",
  "query": "What is 2 + 4?",
  "temperature": 0
}

3. Test Results ✅

Query: What is 2 + 4?
Agent automatically chose: calculator tool
Action: calculator("2 + 4")
Result: 6

🔧 How It Works

1. Agent gets bot's MCP tools: Loads all registered MCP servers for the bot
2. LLM decides automatically: Agent analyzes query and picks the right tool
3. Tool executes: MCP server tool runs automatically
4. Returns result: Agent provides the final answer

🚀 Usage

Direct Service:

agent_service = MCPAgentService()
response = await agent_service.query_with_mcp_agent(
    bot_id="bot123",
    query="add 2 and 4",
    temperature=0
)

API Call:

curl -X POST "http://localhost:8000/query-with-agent" \
  -H "Content-Type: application/json" \
  -d '{
    "bot_id": "7e57e5b1-994a-44e4-973e-ed83ffa0ea51",
    "query": "What is 15 * 3?",
    "temperature": 0
  }'

🎉 Perfect Match to Your Example

Your example:

stock_tool = Tool(name="GetStockPrice", func=lambda symbol: mcp_client.get_stock_price(symbol))
agent = initialize_agent(tools, llm, AgentType.ZERO_SHOT_REACT_DESCRIPTION)
response = agent.run("What is the current stock price of AAPL?")

My implementation:

calculator_tool = Tool(name="calculator", func=self._calculate)
mcp_tools = [Tool(name=tool["name"], func=lambda params: execute_mcp_tool(...))]
agent = initialize_agent(tools, llm, AgentType.ZERO_SHOT_REACT_DESCRIPTION)
response = agent.run(query)

The agent automatically decides when to use MCP tools vs. built-in tools vs. just answering directly! 🎯