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"""
Autonomous AI Agent with MCP Tool Calling
This agent uses Claude 3.5 Sonnet (or compatible LLM) to autonomously
decide which MCP tools to call based on the user's task.
This is TRUE AI-driven MCP usage - no hardcoded workflow!
"""
import os
import json
import uuid
import logging
from typing import List, Dict, Any, AsyncGenerator
from anthropic import AsyncAnthropic
from mcp.tools.definitions import MCP_TOOLS
from mcp.registry import MCPRegistry
logger = logging.getLogger(__name__)
class AutonomousMCPAgent:
"""
AI Agent that autonomously uses MCP servers as tools.
Key Features:
- Uses Claude 3.5 Sonnet for tool calling
- Autonomously decides which MCP tools to use
- No hardcoded workflow - AI makes all decisions
- Proper MCP protocol implementation
"""
def __init__(self, mcp_registry: MCPRegistry, api_key: str = None):
"""
Initialize the autonomous agent
Args:
mcp_registry: MCP registry with all servers
api_key: Anthropic API key (or use ANTHROPIC_API_KEY env var)
"""
self.mcp_registry = mcp_registry
self.api_key = api_key or os.getenv("ANTHROPIC_API_KEY")
if not self.api_key:
raise ValueError(
"Anthropic API key required for autonomous agent. "
"Set ANTHROPIC_API_KEY environment variable or pass api_key parameter."
)
self.client = AsyncAnthropic(api_key=self.api_key)
self.model = "claude-3-5-sonnet-20241022"
# System prompt for the agent
self.system_prompt = """You are an autonomous AI agent for B2B sales automation.
You have access to MCP (Model Context Protocol) servers that provide tools for:
- Web search (find company information, news, insights)
- Data storage (save prospects, companies, contacts, facts)
- Email management (send emails, track threads)
- Calendar (schedule meetings)
Your goal is to help with B2B sales tasks like:
- Finding and researching potential customers
- Enriching company data with facts and insights
- Finding decision-maker contacts
- Drafting personalized outreach emails
- Managing prospect pipeline
IMPORTANT:
1. Think step-by-step about what information you need
2. Use tools autonomously to gather information
3. Save important data to the store for persistence
4. Be thorough in research before making recommendations
5. Always check suppression list before suggesting email sends
You should:
- Search for company information when needed
- Save prospects and companies to the database
- Find and save contacts
- Generate personalized outreach based on research
- Track your progress and findings
Work autonomously - decide which tools to use and when!"""
logger.info(f"Autonomous MCP Agent initialized with model: {self.model}")
async def run(
self,
task: str,
max_iterations: int = 15
) -> AsyncGenerator[Dict[str, Any], None]:
"""
Run the agent autonomously on a task.
The agent will:
1. Understand the task
2. Decide which MCP tools to call
3. Execute tools autonomously
4. Continue until task is complete or max iterations reached
Args:
task: The task to complete (e.g., "Research and create outreach for Shopify")
max_iterations: Maximum tool calls to prevent infinite loops
Yields:
Events showing agent's progress and tool calls
"""
yield {
"type": "agent_start",
"message": f"🤖 Autonomous AI Agent starting task: {task}",
"model": self.model
}
# Initialize conversation
messages = [
{
"role": "user",
"content": task
}
]
iteration = 0
while iteration < max_iterations:
iteration += 1
yield {
"type": "iteration_start",
"iteration": iteration,
"message": f"🔄 Iteration {iteration}: AI deciding next action..."
}
try:
# Call Claude with tools
response = await self.client.messages.create(
model=self.model,
max_tokens=4096,
system=self.system_prompt,
messages=messages,
tools=MCP_TOOLS
)
# Add assistant response to conversation
messages.append({
"role": "assistant",
"content": response.content
})
# Check if AI wants to use tools
tool_calls = [block for block in response.content if block.type == "tool_use"]
if not tool_calls:
# AI is done - no more tools to call
final_text = next(
(block.text for block in response.content if hasattr(block, "text")),
"Task completed!"
)
yield {
"type": "agent_complete",
"message": f"✅ Task complete!",
"final_response": final_text,
"iterations": iteration
}
break
# Execute tool calls
tool_results = []
for tool_call in tool_calls:
tool_name = tool_call.name
tool_input = tool_call.input
yield {
"type": "tool_call",
"tool": tool_name,
"input": tool_input,
"message": f"🔧 AI calling tool: {tool_name}"
}
# Execute the MCP tool
try:
result = await self._execute_mcp_tool(tool_name, tool_input)
yield {
"type": "tool_result",
"tool": tool_name,
"result": result,
"message": f"✓ Tool {tool_name} completed"
}
# Add tool result to conversation
tool_results.append({
"type": "tool_result",
"tool_use_id": tool_call.id,
"content": json.dumps(result, default=str)
})
except Exception as e:
error_msg = str(e)
logger.error(f"Tool execution failed: {tool_name} - {error_msg}")
yield {
"type": "tool_error",
"tool": tool_name,
"error": error_msg,
"message": f"❌ Tool {tool_name} failed: {error_msg}"
}
tool_results.append({
"type": "tool_result",
"tool_use_id": tool_call.id,
"content": json.dumps({"error": error_msg}),
"is_error": True
})
# Add tool results to conversation
messages.append({
"role": "user",
"content": tool_results
})
except Exception as e:
logger.error(f"Agent iteration failed: {e}")
yield {
"type": "agent_error",
"error": str(e),
"message": f"❌ Agent error: {str(e)}"
}
break
if iteration >= max_iterations:
yield {
"type": "agent_max_iterations",
"message": f"⚠️ Reached maximum iterations ({max_iterations})",
"iterations": iteration
}
async def _execute_mcp_tool(self, tool_name: str, tool_input: Dict[str, Any]) -> Any:
"""
Execute an MCP tool by routing to the appropriate MCP server.
This is where we actually call the MCP servers!
"""
# ============ SEARCH MCP SERVER ============
if tool_name == "search_web":
query = tool_input["query"]
max_results = tool_input.get("max_results", 5)
results = await self.mcp_registry.search.query(query, max_results=max_results)
return {
"results": results,
"count": len(results)
}
elif tool_name == "search_news":
query = tool_input["query"]
max_results = tool_input.get("max_results", 5)
results = await self.mcp_registry.search.query(f"{query} news", max_results=max_results)
return {
"results": results,
"count": len(results)
}
# ============ STORE MCP SERVER ============
elif tool_name == "save_prospect":
prospect_data = {
"id": tool_input.get("prospect_id", str(uuid.uuid4())),
"company": {
"id": tool_input.get("company_id"),
"name": tool_input.get("company_name"),
"domain": tool_input.get("company_domain")
},
"fit_score": tool_input.get("fit_score", 0),
"status": tool_input.get("status", "new"),
"metadata": tool_input.get("metadata", {})
}
result = await self.mcp_registry.store.save_prospect(prospect_data)
return {"status": result, "prospect_id": prospect_data["id"]}
elif tool_name == "get_prospect":
prospect_id = tool_input["prospect_id"]
prospect = await self.mcp_registry.store.get_prospect(prospect_id)
return prospect or {"error": "Prospect not found"}
elif tool_name == "list_prospects":
prospects = await self.mcp_registry.store.list_prospects()
status_filter = tool_input.get("status")
if status_filter:
prospects = [p for p in prospects if p.get("status") == status_filter]
return {
"prospects": prospects,
"count": len(prospects)
}
elif tool_name == "save_company":
company_data = {
"id": tool_input.get("company_id", str(uuid.uuid4())),
"name": tool_input["name"],
"domain": tool_input["domain"],
"industry": tool_input.get("industry"),
"description": tool_input.get("description"),
"employee_count": tool_input.get("employee_count")
}
result = await self.mcp_registry.store.save_company(company_data)
return {"status": result, "company_id": company_data["id"]}
elif tool_name == "get_company":
company_id = tool_input["company_id"]
company = await self.mcp_registry.store.get_company(company_id)
return company or {"error": "Company not found"}
elif tool_name == "save_fact":
fact_data = {
"id": tool_input.get("fact_id", str(uuid.uuid4())),
"company_id": tool_input["company_id"],
"fact_type": tool_input["fact_type"],
"content": tool_input["content"],
"source_url": tool_input.get("source_url"),
"confidence_score": tool_input.get("confidence_score", 0.8)
}
result = await self.mcp_registry.store.save_fact(fact_data)
return {"status": result, "fact_id": fact_data["id"]}
elif tool_name == "save_contact":
contact_data = {
"id": tool_input.get("contact_id", str(uuid.uuid4())),
"company_id": tool_input["company_id"],
"email": tool_input["email"],
"first_name": tool_input.get("first_name"),
"last_name": tool_input.get("last_name"),
"title": tool_input.get("title"),
"seniority": tool_input.get("seniority")
}
result = await self.mcp_registry.store.save_contact(contact_data)
return {"status": result, "contact_id": contact_data["id"]}
elif tool_name == "list_contacts_by_domain":
domain = tool_input["domain"]
contacts = await self.mcp_registry.store.list_contacts_by_domain(domain)
return {
"contacts": contacts,
"count": len(contacts)
}
elif tool_name == "check_suppression":
supp_type = tool_input["suppression_type"]
value = tool_input["value"]
is_suppressed = await self.mcp_registry.store.check_suppression(supp_type, value)
return {
"suppressed": is_suppressed,
"value": value,
"type": supp_type
}
# ============ EMAIL MCP SERVER ============
elif tool_name == "send_email":
to = tool_input["to"]
subject = tool_input["subject"]
body = tool_input["body"]
prospect_id = tool_input["prospect_id"]
thread_id = await self.mcp_registry.email.send(to, subject, body, prospect_id)
return {
"status": "sent",
"thread_id": thread_id,
"to": to
}
elif tool_name == "get_email_thread":
prospect_id = tool_input["prospect_id"]
thread = await self.mcp_registry.email.get_thread(prospect_id)
return thread or {"error": "No email thread found"}
# ============ CALENDAR MCP SERVER ============
elif tool_name == "suggest_meeting_slots":
num_slots = tool_input.get("num_slots", 3)
slots = await self.mcp_registry.calendar.suggest_slots()
return {
"slots": slots[:num_slots],
"count": len(slots[:num_slots])
}
elif tool_name == "generate_calendar_invite":
start_time = tool_input["start_time"]
end_time = tool_input["end_time"]
title = tool_input["title"]
slot = {
"start_iso": start_time,
"end_iso": end_time,
"title": title
}
ics = await self.mcp_registry.calendar.generate_ics(slot)
return {
"ics_content": ics,
"meeting": slot
}
else:
raise ValueError(f"Unknown MCP tool: {tool_name}")
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